Instruction: What field is the article from?

Prompt:
Title: Unveiling Public Perceptions: Machine Learning-Based Sentiment Analysis of COVID-19 Vaccines in India
Abstract: In March 2020, the World Health Organisation declared COVID-19 a global
pandemic as it spread to nearly every country. By mid-2021, India had
introduced three vaccines: Covishield, Covaxin, and Sputnik. To ensure
successful vaccination in a densely populated country like India, understanding
public sentiment was crucial. Social media, particularly Reddit with over 430
million users, played a vital role in disseminating information. This study
employs data mining techniques to analyze Reddit data and gauge Indian
sentiments towards COVID-19 vaccines. Using Python's Text Blob library,
comments are annotated to assess general sentiments. Results show that most
Reddit users in India expressed neutrality about vaccination, posing a
challenge for the Indian government's efforts to vaccinate a significant
portion of the population.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: A Turing Test: Are AI Chatbots Behaviorally Similar to Humans?
Abstract: We administer a Turing Test to AI Chatbots. We examine how Chatbots behave in
a suite of classic behavioral games that are designed to elicit characteristics
such as trust, fairness, risk-aversion, cooperation, \textit{etc.}; as well as
a traditional Big-5 psychological survey that measures personality traits.
ChatGPT-4 passes the Turing Test in that it consistently exhibits human-like
behavioral and personality traits based on a comparison to the behavior of
hundreds of thousands of humans from more than 50 countries. Chatbots also
modify their behavior based on previous experience and contexts ``as if'' they
were learning from the interactions, and change their behavior in response to
different framings of the same strategic situation. Their behaviors are often
distinct from average and modal human behaviors, in which case they tend to
behave on the more altruistic and cooperative end of the distribution. We
estimate that they act as if they are maximizing an average of their own and
partner's payoff.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Unlocking Anticipatory Text Generation: A Constrained Approach for Faithful Decoding with Large Language Models
Abstract: Large Language Models (LLMs) have demonstrated a powerful ability for text
generation. However, achieving optimal results with a given prompt or
instruction can be challenging, especially for billion-sized models.
Additionally, undesired behaviors such as toxicity or hallucinations can
manifest. While much larger models (e.g., ChatGPT) may demonstrate strength in
mitigating these issues, there is still no guarantee of complete prevention. In
this work, we propose formalizing text generation as a future-constrained
generation problem to minimize undesirable behaviors and enforce faithfulness
to instructions. The estimation of future constraint satisfaction, accomplished
using LLMs, guides the text generation process. Our extensive experiments
demonstrate the effectiveness of the proposed approach across three distinct
text generation tasks: keyword-constrained generation (Lin et al., 2020),
toxicity reduction (Gehman et al., 2020), and factual correctness in
question-answering (Gao et al., 2023).

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: The BigCode Project Governance Card
Abstract: This document serves as an overview of the different mechanisms and areas of
governance in the BigCode project. It aims to support transparency by providing
relevant information about choices that were made during the project to the
broader public, and to serve as an example of intentional governance of an open
research project that future endeavors can leverage to shape their own
approach. The first section, Project Structure, covers the project
organization, its stated goals and values, its internal decision processes, and
its funding and resources. The second section, Data and Model Governance,
covers decisions relating to the questions of data subject consent, privacy,
and model release.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Analyze Drivers' Intervention Behavior During Autonomous Driving -- A VR-incorporated Approach
Abstract: Given the rapid advance in ITS technologies, future mobility is pointing to
vehicular autonomy. However, there is still a long way before full automation,
and human intervention is required. This work sheds light on understanding
human drivers' intervention behavior involved in the operation of autonomous
vehicles (AVs) and utilizes this knowledge to improve the perception of
critical driving scenarios. Experiment environments were implemented where the
virtual reality (VR) and traffic micro-simulation are integrated, and tests
were carried out under typical and diverse traffic scenes. Performance
indicators such as the probability of intervention, accident rates are defined
and used to quantify and compare the risk levels. By offering novel insights
into drivers' intervention behavior, this work will help improve the
performances of the automated control under similar scenarios. Furthermore,
such an integrated and immersive tool for autonomous driving studies will be
valuable for research on human-to-automation trust. To the best knowledge of
the authors, this work is among the pioneer works making efforts into such
types of tools.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Differentiable Learning of Generalized Structured Matrices for Efficient Deep Neural Networks
Abstract: This paper investigates efficient deep neural networks (DNNs) to replace
dense unstructured weight matrices with structured ones that possess desired
properties. The challenge arises because the optimal weight matrix structure in
popular neural network models is obscure in most cases and may vary from layer
to layer even in the same network. Prior structured matrices proposed for
efficient DNNs were mostly hand-crafted without a generalized framework to
systematically learn them. To address this issue, we propose a generalized and
differentiable framework to learn efficient structures of weight matrices by
gradient descent. We first define a new class of structured matrices that
covers a wide range of structured matrices in the literature by adjusting the
structural parameters. Then, the frequency-domain differentiable
parameterization scheme based on the Gaussian-Dirichlet kernel is adopted to
learn the structural parameters by proximal gradient descent. Finally, we
introduce an effective initialization method for the proposed scheme. Our
method learns efficient DNNs with structured matrices, achieving lower
complexity and/or higher performance than prior approaches that employ
low-rank, block-sparse, or block-low-rank matrices.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence
Abstract: Artificial intelligence (AI) has been clearly established as a technology
with the potential to revolutionize fields from healthcare to finance - if
developed and deployed responsibly. This is the topic of responsible AI, which
emphasizes the need to develop trustworthy AI systems that minimize bias,
protect privacy, support security, and enhance transparency and accountability.
Explainable AI (XAI) has been broadly considered as a building block for
responsible AI (RAI), with most of the literature considering it as a solution
for improved transparency. This work proposes that XAI and responsible AI are
significantly more deeply entwined. In this work, we explore state-of-the-art
literature on RAI and XAI technologies. Based on our findings, we demonstrate
that XAI can be utilized to ensure fairness, robustness, privacy, security, and
transparency in a wide range of contexts. Our findings lead us to conclude that
XAI is an essential foundation for every pillar of RAI.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Causal Inference Using LLM-Guided Discovery
Abstract: At the core of causal inference lies the challenge of determining reliable
causal graphs solely based on observational data. Since the well-known backdoor
criterion depends on the graph, any errors in the graph can propagate
downstream to effect inference. In this work, we initially show that complete
graph information is not necessary for causal effect inference; the topological
order over graph variables (causal order) alone suffices. Further, given a node
pair, causal order is easier to elicit from domain experts compared to graph
edges since determining the existence of an edge can depend extensively on
other variables. Interestingly, we find that the same principle holds for Large
Language Models (LLMs) such as GPT-3.5-turbo and GPT-4, motivating an automated
method to obtain causal order (and hence causal effect) with LLMs acting as
virtual domain experts. To this end, we employ different prompting strategies
and contextual cues to propose a robust technique of obtaining causal order
from LLMs. Acknowledging LLMs' limitations, we also study possible techniques
to integrate LLMs with established causal discovery algorithms, including
constraint-based and score-based methods, to enhance their performance.
Extensive experiments demonstrate that our approach significantly improves
causal ordering accuracy as compared to discovery algorithms, highlighting the
potential of LLMs to enhance causal inference across diverse fields.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Understanding the Instruction Mixture for Large Language Model Fine-tuning
Abstract: While instructions fine-tuning of large language models (LLMs) has been
proven to enhance performance across various applications, the influence of the
instruction dataset mixture on LLMs has not been thoroughly explored. In this
study, we classify instructions into three main types: NLP downstream tasks,
coding, and general chatting, and investigate their impact on LLMs. Our
findings reveal that specific types of instructions are more beneficial for
particular uses, while it may cause harms to other aspects, emphasizing the
importance of meticulously designing the instruction mixture to maximize model
performance. This study sheds light on the instruction mixture and paves the
way for future research.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: LLMSTEP: LLM proofstep suggestions in Lean
Abstract: We present LLMSTEP, a tool for integrating a language model into the Lean
proof assistant. LLMSTEP is a Lean 4 tactic that sends a user's proof state to
a server hosting a language model. The language model generates suggestions,
which are checked in Lean and displayed to a user in their development
environment. We provide a baseline language model, along with code for
fine-tuning and evaluation to support further development. We provide server
implementations that run on CPU, a CUDA GPU, or a Google Colab notebook, as a
step towards fast, effective language model suggestions for any user.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Ontologies for Models and Algorithms in Applied Mathematics and Related Disciplines
Abstract: In applied mathematics and related disciplines, the
modeling-simulation-optimization workflow is a prominent scheme, with
mathematical models and numerical algorithms playing a crucial role. For these
types of mathematical research data, the Mathematical Research Data Initiative
has developed, merged and implemented ontologies and knowledge graphs. This
contributes to making mathematical research data FAIR by introducing semantic
technology and documenting the mathematical foundations accordingly. Using the
concrete example of microfracture analysis of porous media, it is shown how the
knowledge of the underlying mathematical model and the corresponding numerical
algorithms for its solution can be represented by the ontologies.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Mind the Gap Between Conversations for Improved Long-Term Dialogue Generation
Abstract: Knowing how to end and resume conversations over time is a natural part of
communication, allowing for discussions to span weeks, months, or years. The
duration of gaps between conversations dictates which topics are relevant and
which questions to ask, and dialogue systems which do not explicitly model time
may generate responses that are unnatural. In this work we explore the idea of
making dialogue models aware of time, and present GapChat, a multi-session
dialogue dataset in which the time between each session varies. While the
dataset is constructed in real-time, progress on events in speakers' lives is
simulated in order to create realistic dialogues occurring across a long
timespan. We expose time information to the model and compare different
representations of time and event progress. In human evaluation we show that
time-aware models perform better in metrics that judge the relevance of the
chosen topics and the information gained from the conversation.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: XAI-CLASS: Explanation-Enhanced Text Classification with Extremely Weak Supervision
Abstract: Text classification aims to effectively categorize documents into pre-defined
categories. Traditional methods for text classification often rely on large
amounts of manually annotated training data, making the process time-consuming
and labor-intensive. To address this issue, recent studies have focused on
weakly-supervised and extremely weakly-supervised settings, which require
minimal or no human annotation, respectively. In previous methods of weakly
supervised text classification, pseudo-training data is generated by assigning
pseudo-labels to documents based on their alignment (e.g., keyword matching)
with specific classes. However, these methods ignore the importance of
incorporating the explanations of the generated pseudo-labels, or saliency of
individual words, as additional guidance during the text classification
training process. To address this limitation, we propose XAI-CLASS, a novel
explanation-enhanced extremely weakly-supervised text classification method
that incorporates word saliency prediction as an auxiliary task. XAI-CLASS
begins by employing a multi-round question-answering process to generate
pseudo-training data that promotes the mutual enhancement of class labels and
corresponding explanation word generation. This pseudo-training data is then
used to train a multi-task framework that simultaneously learns both text
classification and word saliency prediction. Extensive experiments on several
weakly-supervised text classification datasets show that XAI-CLASS outperforms
other weakly-supervised text classification methods significantly. Moreover,
experiments demonstrate that XAI-CLASS enhances both model performance and
explainability.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Instruct2Attack: Language-Guided Semantic Adversarial Attacks
Abstract: We propose Instruct2Attack (I2A), a language-guided semantic attack that
generates semantically meaningful perturbations according to free-form language
instructions. We make use of state-of-the-art latent diffusion models, where we
adversarially guide the reverse diffusion process to search for an adversarial
latent code conditioned on the input image and text instruction. Compared to
existing noise-based and semantic attacks, I2A generates more natural and
diverse adversarial examples while providing better controllability and
interpretability. We further automate the attack process with GPT-4 to generate
diverse image-specific text instructions. We show that I2A can successfully
break state-of-the-art deep neural networks even under strong adversarial
defenses, and demonstrate great transferability among a variety of network
architectures.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework
Abstract: The Knapsack Problem is a classic problem in combinatorial optimisation.
Solving these problems may be computationally expensive. Recent years have seen
a growing interest in the use of deep learning methods to approximate the
solutions to such problems. A core problem is how to enforce or encourage
constraint satisfaction in predicted solutions. A promising approach for
predicting solutions to constrained optimisation problems is the Lagrangian
Dual Framework which builds on the method of Lagrangian Relaxation. In this
paper we develop neural network models to approximate Knapsack Problem
solutions using the Lagrangian Dual Framework while improving constraint
satisfaction. We explore the problems of output interpretation and model
selection within this context. Experimental results show strong constraint
satisfaction with a minor reduction of optimality as compared to a baseline
neural network which does not explicitly model the constraints.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: World Models via Policy-Guided Trajectory Diffusion
Abstract: World models are a powerful tool for developing intelligent agents. By
predicting the outcome of a sequence of actions, world models enable policies
to be optimised via on-policy reinforcement learning (RL) using synthetic data,
i.e. in "in imagination". Existing world models are autoregressive in that they
interleave predicting the next state with sampling the next action from the
policy. Prediction error inevitably compounds as the trajectory length grows.
In this work, we propose a novel world modelling approach that is not
autoregressive and generates entire on-policy trajectories in a single pass
through a diffusion model. Our approach, Policy-Guided Trajectory Diffusion
(PolyGRAD), leverages a denoising model in addition to the gradient of the
action distribution of the policy to diffuse a trajectory of initially random
states and actions into an on-policy synthetic trajectory. We analyse the
connections between PolyGRAD, score-based generative models, and
classifier-guided diffusion models. Our results demonstrate that PolyGRAD
outperforms state-of-the-art baselines in terms of trajectory prediction error
for moderate-length trajectories, with the exception of autoregressive
diffusion. At short horizons, PolyGRAD obtains comparable errors to
autoregressive diffusion, but with significantly lower computational
requirements. Our experiments also demonstrate that PolyGRAD enables performant
policies to be trained via on-policy RL in imagination for MuJoCo continuous
control domains. Thus, PolyGRAD introduces a new paradigm for scalable and
non-autoregressive on-policy world modelling.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: LLM360: Towards Fully Transparent Open-Source LLMs
Abstract: The recent surge in open-source Large Language Models (LLMs), such as LLaMA,
Falcon, and Mistral, provides diverse options for AI practitioners and
researchers. However, most LLMs have only released partial artifacts, such as
the final model weights or inference code, and technical reports increasingly
limit their scope to high-level design choices and surface statistics. These
choices hinder progress in the field by degrading transparency into the
training of LLMs and forcing teams to rediscover many details in the training
process. We present LLM360, an initiative to fully open-source LLMs, which
advocates for all training code and data, model checkpoints, and intermediate
results to be made available to the community. The goal of LLM360 is to support
open and collaborative AI research by making the end-to-end LLM training
process transparent and reproducible by everyone. As a first step of LLM360, we
release two 7B parameter LLMs pre-trained from scratch, Amber and CrystalCoder,
including their training code, data, intermediate checkpoints, and analyses (at
https://www.llm360.ai). We are committed to continually pushing the boundaries
of LLMs through this open-source effort. More large-scale and stronger models
are underway and will be released in the future.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: MKA: A Scalable Medical Knowledge Assisted Mechanism for Generative Models on Medical Conversation Tasks
Abstract: Using natural language processing (NLP) technologies to develop medical
chatbots makes the diagnosis of the patient more convenient and efficient,
which is a typical application in healthcare AI. Because of its importance,
lots of research have been come out. Recently, the neural generative models
have shown their impressive ability as the core of chatbot, while it cannot
scale well when directly applied to medical conversation due to the lack of
medical-specific knowledge. To address the limitation, a scalable Medical
Knowledge Assisted mechanism, MKA, is proposed in this paper. The mechanism
aims to assist general neural generative models to achieve better performance
on the medical conversation task. The medical-specific knowledge graph is
designed within the mechanism, which contains 6 types of medical-related
information, including department, drug, check, symptom, disease, food.
Besides, the specific token concatenation policy is defined to effectively
inject medical information into the input data. Evaluation of our method is
carried out on two typical medical datasets, MedDG and MedDialog-CN. The
evaluation results demonstrate that models combined with our mechanism
outperform original methods in multiple automatic evaluation metrics. Besides,
MKA-Bert-GPT achieves state-of-the-art performance. The open-sourced codes are
public:
https://github.com/LIANGKE23/Knowledge_Assisted_Medical_Dialogue_Generation_Mechanism

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: You Only Forward Once: Prediction and Rationalization in A Single Forward Pass
Abstract: Unsupervised rationale extraction aims to extract concise and contiguous text
snippets to support model predictions without any annotated rationale. Previous
studies have used a two-phase framework known as the Rationalizing Neural
Prediction (RNP) framework, which follows a generate-then-predict paradigm.
They assumed that the extracted explanation, called rationale, should be
sufficient to predict the golden label. However, the assumption above deviates
from the original definition and is too strict to perform well. Furthermore,
these two-phase models suffer from the interlocking problem and spurious
correlations. To solve the above problems, we propose a novel single-phase
framework called You Only Forward Once (YOFO), derived from a relaxed version
of rationale where rationales aim to support model predictions rather than make
predictions. In our framework, A pre-trained language model like BERT is
deployed to simultaneously perform prediction and rationalization with less
impact from interlocking or spurious correlations. Directly choosing the
important tokens in an unsupervised manner is intractable. Instead of directly
choosing the important tokens, YOFO gradually removes unimportant tokens during
forward propagation. Through experiments on the BeerAdvocate and Hotel Review
datasets, we demonstrate that our model is able to extract rationales and make
predictions more accurately compared to RNP-based models. We observe an
improvement of up to 18.4\% in token-level F1 compared to previous
state-of-the-art methods. We also conducted analyses and experiments to explore
the extracted rationales and token decay strategies. The results show that YOFO
can extract precise and important rationales while removing unimportant tokens
in the middle part of the model.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Socially Cognizant Robotics for a Technology Enhanced Society
Abstract: Emerging applications of robotics, and concerns about their impact, require
the research community to put human-centric objectives front-and-center. To
meet this challenge, we advocate an interdisciplinary approach, socially
cognizant robotics, which synthesizes technical and social science methods. We
argue that this approach follows from the need to empower stakeholder
participation (from synchronous human feedback to asynchronous societal
assessment) in shaping AI-driven robot behavior at all levels, and leads to a
range of novel research perspectives and problems both for improving robots'
interactions with individuals and impacts on society. Drawing on these
arguments, we develop best practices for socially cognizant robot design that
balance traditional technology-based metrics (e.g. efficiency, precision and
accuracy) with critically important, albeit challenging to measure, human and
society-based metrics.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Trustworthy Large Models in Vision: A Survey
Abstract: The rapid progress of Large Models (LMs) has recently revolutionized various
fields of deep learning with remarkable grades, ranging from Natural Language
Processing (NLP) to Computer Vision (CV). However, LMs are increasingly
challenged and criticized by academia and industry due to their powerful
performance but untrustworthy behavior, which urgently needs to be alleviated
by reliable methods. Despite the abundance of literature on trustworthy LMs in
NLP, a systematic survey specifically delving into the trustworthiness of LMs
in CV remains absent. In order to mitigate this gap, we summarize four relevant
concerns that obstruct the trustworthy usage in vision of LMs in this survey,
including 1) human misuse, 2) vulnerability, 3) inherent issue and 4)
interpretability. By highlighting corresponding challenge, countermeasures, and
discussion in each topic, we hope this survey will facilitate readers'
understanding of this field, promote alignment of LMs with human expectations
and enable trustworthy LMs to serve as welfare rather than disaster for human
society.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning
Abstract: Background: Discovering potential drug-drug interactions (DDIs) is a
long-standing challenge in clinical treatments and drug developments. Recently,
deep learning techniques have been developed for DDI prediction. However, they
generally require a huge number of samples, while known DDIs are rare.
  Methods: In this work, we present KnowDDI, a graph neural network-based
method that addresses the above challenge. KnowDDI enhances drug
representations by adaptively leveraging rich neighborhood information from
large biomedical knowledge graphs. Then, it learns a knowledge subgraph for
each drug-pair to interpret the predicted DDI, where each of the edges is
associated with a connection strength indicating the importance of a known DDI
or resembling strength between a drug-pair whose connection is unknown. Thus,
the lack of DDIs is implicitly compensated by the enriched drug representations
and propagated drug similarities.
  Results: We evaluate KnowDDI on two benchmark DDI datasets. Results show that
KnowDDI obtains the state-of-the-art prediction performance with better
interpretability. We also find that KnowDDI suffers less than existing works
given a sparser knowledge graph. This indicates that the propagated drug
similarities play a more important role in compensating for the lack of DDIs
when the drug representations are less enriched.
  Conclusions: KnowDDI nicely combines the efficiency of deep learning
techniques and the rich prior knowledge in biomedical knowledge graphs. As an
original open-source tool, KnowDDI can help detect possible interactions in a
broad range of relevant interaction prediction tasks, such as protein-protein
interactions, drug-target interactions and disease-gene interactions,
eventually promoting the development of biomedicine and healthcare.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Spectral Temporal Contrastive Learning
Abstract: Learning useful data representations without requiring labels is a
cornerstone of modern deep learning. Self-supervised learning methods,
particularly contrastive learning (CL), have proven successful by leveraging
data augmentations to define positive pairs. This success has prompted a number
of theoretical studies to better understand CL and investigate theoretical
bounds for downstream linear probing tasks. This work is concerned with the
temporal contrastive learning (TCL) setting where the sequential structure of
the data is used instead to define positive pairs, which is more commonly used
in RL and robotics contexts. In this paper, we adapt recent work on Spectral CL
to formulate Spectral Temporal Contrastive Learning (STCL). We discuss a
population loss based on a state graph derived from a time-homogeneous
reversible Markov chain with uniform stationary distribution. The STCL loss
enables to connect the linear probing performance to the spectral properties of
the graph, and can be estimated by considering previously observed data
sequences as an ensemble of MCMC chains.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: LLM-FP4: 4-Bit Floating-Point Quantized Transformers
Abstract: We propose LLM-FP4 for quantizing both weights and activations in large
language models (LLMs) down to 4-bit floating-point values, in a post-training
manner. Existing post-training quantization (PTQ) solutions are primarily
integer-based and struggle with bit widths below 8 bits. Compared to integer
quantization, floating-point (FP) quantization is more flexible and can better
handle long-tail or bell-shaped distributions, and it has emerged as a default
choice in many hardware platforms. One characteristic of FP quantization is
that its performance largely depends on the choice of exponent bits and
clipping range. In this regard, we construct a strong FP-PTQ baseline by
searching for the optimal quantization parameters. Furthermore, we observe a
high inter-channel variance and low intra-channel variance pattern in
activation distributions, which adds activation quantization difficulty. We
recognize this pattern to be consistent across a spectrum of transformer models
designed for diverse tasks, such as LLMs, BERT, and Vision Transformer models.
To tackle this, we propose per-channel activation quantization and show that
these additional scaling factors can be reparameterized as exponential biases
of weights, incurring a negligible cost. Our method, for the first time, can
quantize both weights and activations in the LLaMA-13B to only 4-bit and
achieves an average score of 63.1 on the common sense zero-shot reasoning
tasks, which is only 5.8 lower than the full-precision model, significantly
outperforming the previous state-of-the-art by 12.7 points. Code is available
at: https://github.com/nbasyl/LLM-FP4.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Deep Learning for Spatiotemporal Big Data: A Vision on Opportunities and Challenges
Abstract: With advancements in GPS, remote sensing, and computational simulation, an
enormous volume of spatiotemporal data is being collected at an increasing
speed from various application domains, spanning Earth sciences, agriculture,
smart cities, and public safety. Such emerging geospatial and spatiotemporal
big data, coupled with recent advances in deep learning technologies, foster
new opportunities to solve problems that have not been possible before. For
instance, remote sensing researchers can potentially train a foundation model
using Earth imagery big data for numerous land cover and land use modeling
tasks. Coastal modelers can train AI surrogates to speed up numerical
simulations. However, the distinctive characteristics of spatiotemporal big
data pose new challenges for deep learning technologies. This vision paper
introduces various types of spatiotemporal big data, discusses new research
opportunities in the realm of deep learning applied to spatiotemporal big data,
lists the unique challenges, and identifies several future research needs.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: LANS: A Layout-Aware Neural Solver for Plane Geometry Problem
Abstract: Geometry problem solving (GPS) is a challenging mathematical reasoning task
requiring multi-modal understanding, fusion and reasoning. Existing neural
solvers take GPS as a vision-language task but be short in the representation
of geometry diagrams which carry rich and complex layout information. In this
paper, we propose a layout-aware neural solver named LANS, integrated with two
new modules: multimodal layout-aware pre-trained language model (MLA-PLM) and
layout-aware fusion attention (LA-FA). MLA-PLM adopts structural and semantic
pre-training (SSP) to implement global relationship modeling, and point
matching pre-training (PMP) to achieve alignment between visual points and
textual points. LA-FA employs a layout-aware attention mask to realize
point-guided cross-modal fusion for further boosting layout awareness of LANS.
Extensive experiments on datasets Geometry3K and PGPS9K validate the
effectiveness of the layout-aware modules and superior problem solving
performance of our LANS solver, over existing symbolic solvers and neural
solvers. The code will make public available soon.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Meta-Diversity Search in Complex Systems, A Recipe for Artificial Open-Endedness ?
Abstract: Can we build an artificial system that would be able to generate endless
surprises if ran "forever" in Minecraft? While there is not a single path
toward solving that grand challenge, this article presents what we believe to
be some working ingredients for the endless generation of novel increasingly
complex artifacts in Minecraft. Our framework for an open-ended system includes
two components: a complex system used to recursively grow and complexify
artifacts over time, and a discovery algorithm that leverages the concept of
meta-diversity search. Since complex systems have shown to enable the emergence
of considerable complexity from set of simple rules, we believe them to be
great candidates to generate all sort of artifacts in Minecraft. Yet, the space
of possible artifacts that can be generated by these systems is often unknown,
challenging to characterize and explore. Therefore automating the long-term
discovery of novel and increasingly complex artifacts in these systems is an
exciting research field. To approach these challenges, we formulate the problem
of meta-diversity search where an artificial "discovery assistant"
incrementally learns a diverse set of representations to characterize behaviors
and searches to discover diverse patterns within each of them. A successful
discovery assistant should continuously seek for novel sources of diversities
while being able to quickly specialize the search toward a new unknown type of
diversity. To implement those ideas in the Minecraft environment, we simulate
an artificial "chemistry" system based on Lenia continuous cellular automaton
for generating artifacts, as well as an artificial "discovery assistant"
(called Holmes) for the artifact-discovery process. Holmes incrementally learns
a hierarchy of modular representations to characterize divergent sources of
diversity and uses a goal-based intrinsically-motivated exploration as the
diversity search strategy.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia
Abstract: Agent-based modeling has been around for decades, and applied widely across
the social and natural sciences. The scope of this research method is now
poised to grow dramatically as it absorbs the new affordances provided by Large
Language Models (LLM)s. Generative Agent-Based Models (GABM) are not just
classic Agent-Based Models (ABM)s where the agents talk to one another. Rather,
GABMs are constructed using an LLM to apply common sense to situations, act
"reasonably", recall common semantic knowledge, produce API calls to control
digital technologies like apps, and communicate both within the simulation and
to researchers viewing it from the outside. Here we present Concordia, a
library to facilitate constructing and working with GABMs. Concordia makes it
easy to construct language-mediated simulations of physically- or
digitally-grounded environments. Concordia agents produce their behavior using
a flexible component system which mediates between two fundamental operations:
LLM calls and associative memory retrieval. A special agent called the Game
Master (GM), which was inspired by tabletop role-playing games, is responsible
for simulating the environment where the agents interact. Agents take actions
by describing what they want to do in natural language. The GM then translates
their actions into appropriate implementations. In a simulated physical world,
the GM checks the physical plausibility of agent actions and describes their
effects. In digital environments simulating technologies such as apps and
services, the GM may handle API calls to integrate with external tools such as
general AI assistants (e.g., Bard, ChatGPT), and digital apps (e.g., Calendar,
Email, Search, etc.). Concordia was designed to support a wide array of
applications both in scientific research and for evaluating performance of real
digital services by simulating users and/or generating synthetic data.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: UAVs and Birds: Enhancing Short-Range Navigation through Budgerigar Flight Studies
Abstract: This study delves into the flight behaviors of Budgerigars (Melopsittacus
undulatus) to gain insights into their flight trajectories and movements. Using
3D reconstruction from stereo video camera recordings, we closely examine the
velocity and acceleration patterns during three flight motion takeoff, flying
and landing. The findings not only contribute to our understanding of bird
behaviors but also hold significant implications for the advancement of
algorithms in Unmanned Aerial Vehicles (UAVs). The research aims to bridge the
gap between biological principles observed in birds and the application of
these insights in developing more efficient and autonomous UAVs. In the context
of the increasing use of drones, this study focuses on the biologically
inspired principles drawn from bird behaviors, particularly during takeoff,
flying and landing flight, to enhance UAV capabilities. The dataset created for
this research sheds light on Budgerigars' takeoff, flying, and landing
techniques, emphasizing their ability to control speed across different
situations and surfaces. The study underscores the potential of incorporating
these principles into UAV algorithms, addressing challenges related to
short-range navigation, takeoff, flying, and landing.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Variational Exploration Module VEM: A Cloud-Native Optimization and Validation Tool for Geospatial Modeling and AI Workflows
Abstract: Geospatial observations combined with computational models have become key to
understanding the physical systems of our environment and enable the design of
best practices to reduce societal harm. Cloud-based deployments help to scale
up these modeling and AI workflows. Yet, for practitioners to make robust
conclusions, model tuning and testing is crucial, a resource intensive process
which involves the variation of model input variables. We have developed the
Variational Exploration Module which facilitates the optimization and
validation of modeling workflows deployed in the cloud by orchestrating
workflow executions and using Bayesian and machine learning-based methods to
analyze model behavior. User configurations allow the combination of diverse
sampling strategies in multi-agent environments. The flexibility and robustness
of the model-agnostic module is demonstrated using real-world applications.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: Identifying Linear Relational Concepts in Large Language Models
Abstract: Transformer language models (LMs) have been shown to represent concepts as
directions in the latent space of hidden activations. However, for any given
human-interpretable concept, how can we find its direction in the latent space?
We present a technique called linear relational concepts (LRC) for finding
concept directions corresponding to human-interpretable concepts at a given
hidden layer in a transformer LM by first modeling the relation between subject
and object as a linear relational embedding (LRE). While the LRE work was
mainly presented as an exercise in understanding model representations, we find
that inverting the LRE while using earlier object layers results in a powerful
technique to find concept directions that both work well as a classifier and
causally influence model outputs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models
Abstract: When a damaging earthquake occurs, immediate information about casualties is
critical for time-sensitive decision-making by emergency response and aid
agencies in the first hours and days. Systems such as Prompt Assessment of
Global Earthquakes for Response (PAGER) by the U.S. Geological Survey (USGS)
were developed to provide a forecast within about 30 minutes of any significant
earthquake globally. Traditional systems for estimating human loss in disasters
often depend on manually collected early casualty reports from global media, a
process that's labor-intensive and slow with notable time delays. Recently,
some systems have employed keyword matching and topic modeling to extract
relevant information from social media. However, these methods struggle with
the complex semantics in multilingual texts and the challenge of interpreting
ever-changing, often conflicting reports of death and injury numbers from
various unverified sources on social media platforms. In this work, we
introduce an end-to-end framework to significantly improve the timeliness and
accuracy of global earthquake-induced human loss forecasting using
multi-lingual, crowdsourced social media. Our framework integrates (1) a
hierarchical casualty extraction model built upon large language models, prompt
design, and few-shot learning to retrieve quantitative human loss claims from
social media, (2) a physical constraint-aware, dynamic-truth discovery model
that discovers the truthful human loss from massive noisy and potentially
conflicting human loss claims, and (3) a Bayesian updating loss projection
model that dynamically updates the final loss estimation using discovered
truths. We test the framework in real-time on a series of global earthquake
events in 2021 and 2022 and show that our framework streamlines casualty data
retrieval, achieving speed and accuracy comparable to manual methods by USGS.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows
Abstract: A Federated Learning (FL) system typically consists of two core processing
entities: the federation controller and the learners. The controller is
responsible for managing the execution of FL workflows across learners and the
learners for training and evaluating federated models over their private
datasets. While executing an FL workflow, the FL system has no control over the
computational resources or data of the participating learners. Still, it is
responsible for other operations, such as model aggregation, task dispatching,
and scheduling. These computationally heavy operations generally need to be
handled by the federation controller. Even though many FL systems have been
recently proposed to facilitate the development of FL workflows, most of these
systems overlook the scalability of the controller. To meet this need, we
designed and developed a novel FL system called MetisFL, where the federation
controller is the first-class citizen. MetisFL re-engineers all the operations
conducted by the federation controller to accelerate the training of
large-scale FL workflows. By quantitatively comparing MetisFL against other
state-of-the-art FL systems, we empirically demonstrate that MetisFL leads to a
10-fold wall-clock time execution boost across a wide range of challenging FL
workflows with increasing model sizes and federation sites.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning
Abstract: Table reasoning has shown remarkable progress in a wide range of table-based
tasks. These challenging tasks require reasoning over both free-form natural
language (NL) questions and semi-structured tabular data. However, previous
table reasoning solutions suffer from significant performance degradation on
"huge" tables. In addition, most existing methods struggle to reason over
complex questions since they lack essential information or they are scattered
in different places. To alleviate these challenges, we exploit a table
provider, namely TAP4LLM, on versatile sampling, augmentation, and packing
methods to achieve effective semi-structured data reasoning using large
language models (LLMs), which 1) decompose raw tables into sub-tables with
specific rows or columns based on the rules or semantic similarity; 2) augment
table information by extracting semantic and statistical metadata from raw
tables while retrieving relevant knowledge from trustworthy knowledge sources
(e.g., Wolfram Alpha, Wikipedia); 3) pack sampled tables with augmented
knowledge into sequence prompts for LLMs reasoning while balancing the token
allocation trade-off. We show that TAP4LLM allows for different components as
plug-ins, enhancing LLMs' understanding of structured data in diverse tabular
tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: CLOMO: Counterfactual Logical Modification with Large Language Models
Abstract: In this study, we delve into the realm of counterfactual reasoning
capabilities of large language models (LLMs). Our primary objective is to
cultivate the counterfactual thought processes within LLMs and rigorously
assess these processes for their validity. Specifically, we introduce a novel
task, Counterfactual Logical Modification (CLOMO), and a high-quality
human-annotated benchmark. In this task, LLMs must adeptly alter a given
argumentative text to uphold a predetermined logical relationship. To
effectively evaluate a generation model's counterfactual capabilities, we
propose an innovative evaluation metric, the LogicAware Counterfactual Score to
directly evaluate the natural language output of LLMs instead of modeling the
task as a multiple-choice problem. Analysis shows that the proposed automatic
metric aligns well with human preference. Our experimental results show that
while LLMs demonstrate a notable capacity for logical counterfactual thinking,
there remains a discernible gap between their current abilities and human
performance.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Collect and Connect Data Leaves to Feature Concepts: Interactive Graph Generation Toward Well-being
Abstract: Feature concepts and data leaves have been invented using datasets to foster
creative thoughts for creating well-being in daily life. The idea, simply put,
is to attach selected and collected data leaves that are summaries of event
flows to be discovered from corresponding datasets, on the target feature
concept representing the well-being aimed. A graph of existing or expected
datasets to be attached to a feature concept is generated semi-automatically.
Rather than sheer automated generative AI, our work addresses the process of
generative artificial and natural intelligence to create the basis for data use
and reuse.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: ArabIcros: AI-Powered Arabic Crossword Puzzle Generation for Educational Applications
Abstract: This paper presents the first Arabic crossword puzzle generator driven by
advanced AI technology. Leveraging cutting-edge large language models including
GPT4, GPT3-Davinci, GPT3-Curie, GPT3-Babbage, GPT3-Ada, and BERT, the system
generates distinctive and challenging clues. Based on a dataset comprising over
50,000 clue-answer pairs, the generator employs fine-tuning, few/zero-shot
learning strategies, and rigorous quality-checking protocols to enforce the
generation of high-quality clue-answer pairs. Importantly, educational
crosswords contribute to enhancing memory, expanding vocabulary, and promoting
problem-solving skills, thereby augmenting the learning experience through a
fun and engaging approach, reshaping the landscape of traditional learning
methods. The overall system can be exploited as a powerful educational tool
that amalgamates AI and innovative learning techniques, heralding a
transformative era for Arabic crossword puzzles and the intersection of
technology and education.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Fair Text-to-Image Diffusion via Fair Mapping
Abstract: In this paper, we address the limitations of existing text-to-image diffusion
models in generating demographically fair results when given human-related
descriptions. These models often struggle to disentangle the target language
context from sociocultural biases, resulting in biased image generation. To
overcome this challenge, we propose Fair Mapping, a general, model-agnostic,
and lightweight approach that modifies a pre-trained text-to-image model by
controlling the prompt to achieve fair image generation. One key advantage of
our approach is its high efficiency. The training process only requires
updating a small number of parameters in an additional linear mapping network.
This not only reduces the computational cost but also accelerates the
optimization process. We first demonstrate the issue of bias in generated
results caused by language biases in text-guided diffusion models. By
developing a mapping network that projects language embeddings into an unbiased
space, we enable the generation of relatively balanced demographic results
based on a keyword specified in the prompt. With comprehensive experiments on
face image generation, we show that our method significantly improves image
generation performance when prompted with descriptions related to human faces.
By effectively addressing the issue of bias, we produce more fair and diverse
image outputs. This work contributes to the field of text-to-image generation
by enhancing the ability to generate images that accurately reflect the
intended demographic characteristics specified in the text.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Invariance Measures for Neural Networks
Abstract: Invariances in neural networks are useful and necessary for many tasks.
However, the representation of the invariance of most neural network models has
not been characterized. We propose measures to quantify the invariance of
neural networks in terms of their internal representation. The measures are
efficient and interpretable, and can be applied to any neural network model.
They are also more sensitive to invariance than previously defined measures. We
validate the measures and their properties in the domain of affine
transformations and the CIFAR10 and MNIST datasets, including their stability
and interpretability. Using the measures, we perform a first analysis of CNN
models and show that their internal invariance is remarkably stable to random
weight initializations, but not to changes in dataset or transformation. We
believe the measures will enable new avenues of research in invariance
representation.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning
Abstract: Few-shot class-incremental learning (FSCIL) aims to build machine learning
model that can continually learn new concepts from a few data samples, without
forgetting knowledge of old classes.
  The challenges of FSCIL lies in the limited data of new classes, which not
only lead to significant overfitting issues but also exacerbates the notorious
catastrophic forgetting problems. As proved in early studies, building sample
relationships is beneficial for learning from few-shot samples. In this paper,
we promote the idea to the incremental scenario, and propose a Sample-to-Class
(S2C) graph learning method for FSCIL.
  Specifically, we propose a Sample-level Graph Network (SGN) that focuses on
analyzing sample relationships within a single session. This network helps
aggregate similar samples, ultimately leading to the extraction of more refined
class-level features.
  Then, we present a Class-level Graph Network (CGN) that establishes
connections across class-level features of both new and old classes. This
network plays a crucial role in linking the knowledge between different
sessions and helps improve overall learning in the FSCIL scenario. Moreover, we
design a multi-stage strategy for training S2C model, which mitigates the
training challenges posed by limited data in the incremental process.
  The multi-stage training strategy is designed to build S2C graph from base to
few-shot stages, and improve the capacity via an extra pseudo-incremental
stage. Experiments on three popular benchmark datasets show that our method
clearly outperforms the baselines and sets new state-of-the-art results in
FSCIL.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: PAC Privacy Preserving Diffusion Models
Abstract: Data privacy protection is garnering increased attention among researchers.
Diffusion models (DMs), particularly with strict differential privacy, can
potentially produce images with both high privacy and visual quality. However,
challenges arise in ensuring robust protection in privatizing specific data
attributes, areas where current models often fall short. To address these
challenges, we introduce the PAC Privacy Preserving Diffusion Model, a model
leverages diffusion principles and ensure Probably Approximately Correct (PAC)
privacy. We enhance privacy protection by integrating a private classifier
guidance into the Langevin Sampling Process. Additionally, recognizing the gap
in measuring the privacy of models, we have developed a novel metric to gauge
privacy levels. Our model, assessed with this new metric and supported by
Gaussian matrix computations for the PAC bound, has shown superior performance
in privacy protection over existing leading private generative models according
to benchmark tests.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Graph-Guided Reasoning for Multi-Hop Question Answering in Large Language Models
Abstract: Chain-of-Thought (CoT) prompting has boosted the multi-step reasoning
capabilities of Large Language Models (LLMs) by generating a series of
rationales before the final answer. We analyze the reasoning paths generated by
CoT and find two issues in multi-step reasoning: (i) Generating rationales
irrelevant to the question, (ii) Unable to compose subquestions or queries for
generating/retrieving all the relevant information. To address them, we propose
a graph-guided CoT prompting method, which guides the LLMs to reach the correct
answer with graph representation/verification steps. Specifically, we first
leverage LLMs to construct a "question/rationale graph" by using knowledge
extraction prompting given the initial question and the rationales generated in
the previous steps. Then, the graph verification step diagnoses the current
rationale triplet by comparing it with the existing question/rationale graph to
filter out irrelevant rationales and generate follow-up questions to obtain
relevant information. Additionally, we generate CoT paths that exclude the
extracted graph information to represent the context information missed from
the graph extraction. Our graph-guided reasoning method shows superior
performance compared to previous CoT prompting and the variants on multi-hop
question answering benchmark datasets.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution Strategies
Abstract: Federated learning (FL) is an emerging paradigm for training deep neural
networks (DNNs) in distributed manners. Current FL approaches all suffer from
high communication overhead and information leakage. In this work, we present a
federated learning algorithm based on evolution strategies (FedES), a
zeroth-order training method. Instead of transmitting model parameters, FedES
only communicates loss values, and thus has very low communication overhead.
Moreover, a third party is unable to estimate gradients without knowing the
pre-shared seed, which protects data privacy. Experimental results demonstrate
FedES can achieve the above benefits while keeping convergence performance the
same as that with back propagation methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Is Robustness Transferable across Languages in Multilingual Neural Machine Translation?
Abstract: Robustness, the ability of models to maintain performance in the face of
perturbations, is critical for developing reliable NLP systems. Recent studies
have shown promising results in improving the robustness of models through
adversarial training and data augmentation. However, in machine translation,
most of these studies have focused on bilingual machine translation with a
single translation direction. In this paper, we investigate the transferability
of robustness across different languages in multilingual neural machine
translation. We propose a robustness transfer analysis protocol and conduct a
series of experiments. In particular, we use character-, word-, and multi-level
noises to attack the specific translation direction of the multilingual neural
machine translation model and evaluate the robustness of other translation
directions. Our findings demonstrate that the robustness gained in one
translation direction can indeed transfer to other translation directions.
Additionally, we empirically find scenarios where robustness to character-level
noise and word-level noise is more likely to transfer.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Improving Medical Report Generation with Adapter Tuning and Knowledge Enhancement in Vision-Language Foundation Models
Abstract: Medical report generation demands automatic creation of coherent and precise
descriptions for medical images. However, the scarcity of labelled medical
image-report pairs poses formidable challenges in developing large-scale neural
networks capable of harnessing the potential of artificial intelligence,
exemplified by large language models. This study builds upon the
state-of-the-art vision-language pre-training and fine-tuning approach, BLIP-2,
to customize general large-scale foundation models. Integrating adapter tuning
and a medical knowledge enhancement loss, our model significantly improves
accuracy and coherence. Validation on the dataset of ImageCLEFmedical 2023
demonstrates our model's prowess, achieving the best-averaged results against
several state-of-the-art methods. Significant improvements in ROUGE and CIDEr
underscore our method's efficacy, highlighting promising outcomes for the rapid
medical-domain adaptation of the vision-language foundation models in
addressing challenges posed by data scarcity.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition
Abstract: Federated Learning (FL) is a promising research paradigm that enables the
collaborative training of machine learning models among various parties without
the need for sensitive information exchange. Nonetheless, retaining data in
individual clients introduces fundamental challenges to achieving performance
on par with centrally trained models. Our study provides an extensive review of
federated learning applied to visual recognition. It underscores the critical
role of thoughtful architectural design choices in achieving optimal
performance, a factor often neglected in the FL literature. Many existing FL
solutions are tested on shallow or simple networks, which may not accurately
reflect real-world applications. This practice restricts the transferability of
research findings to large-scale visual recognition models. Through an in-depth
analysis of diverse cutting-edge architectures such as convolutional neural
networks, transformers, and MLP-mixers, we experimentally demonstrate that
architectural choices can substantially enhance FL systems' performance,
particularly when handling heterogeneous data. We study 19 visual recognition
models from five different architectural families on four challenging FL
datasets. We also re-investigate the inferior performance of convolution-based
architectures in the FL setting and analyze the influence of normalization
layers on the FL performance. Our findings emphasize the importance of
architectural design for computer vision tasks in practical scenarios,
effectively narrowing the performance gap between federated and centralized
learning. Our source code is available at
https://github.com/sarapieri/fed_het.git.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype
Abstract: The accurate classification of lymphoma subtypes using hematoxylin and eosin
(H&E)-stained tissue is complicated by the wide range of morphological features
these cancers can exhibit. We present LymphoML - an interpretable machine
learning method that identifies morphologic features that correlate with
lymphoma subtypes. Our method applies steps to process H&E-stained tissue
microarray cores, segment nuclei and cells, compute features encompassing
morphology, texture, and architecture, and train gradient-boosted models to
make diagnostic predictions. LymphoML's interpretable models, developed on a
limited volume of H&E-stained tissue, achieve non-inferior diagnostic accuracy
to pathologists using whole-slide images and outperform black box deep-learning
on a dataset of 670 cases from Guatemala spanning 8 lymphoma subtypes. Using
SHapley Additive exPlanation (SHAP) analysis, we assess the impact of each
feature on model prediction and find that nuclear shape features are most
discriminative for DLBCL (F1-score: 78.7%) and classical Hodgkin lymphoma
(F1-score: 74.5%). Finally, we provide the first demonstration that a model
combining features from H&E-stained tissue with features from a standardized
panel of 6 immunostains results in a similar diagnostic accuracy (85.3%) to a
46-stain panel (86.1%).

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Oasis: Data Curation and Assessment System for Pretraining of Large Language Models
Abstract: Data is one of the most critical elements in building a large language model.
However, existing systems either fail to customize a corpus curation pipeline
or neglect to leverage comprehensive corpus assessment for iterative
optimization of the curation. To this end, we present a pretraining corpus
curation and assessment platform called Oasis -- a one-stop system for data
quality improvement and quantification with user-friendly interactive
interfaces. Specifically, the interactive modular rule filter module can devise
customized rules according to explicit feedback. The debiased neural filter
module builds the quality classification dataset in a negative-centric manner
to remove the undesired bias. The adaptive document deduplication module could
execute large-scale deduplication with limited memory resources. These three
parts constitute the customized data curation module. And in the holistic data
assessment module, a corpus can be assessed in local and global views, with
three evaluation means including human, GPT-4, and heuristic metrics. We
exhibit a complete process to use Oasis for the curation and assessment of
pretraining data. In addition, an 800GB bilingual corpus curated by Oasis is
publicly released.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations
Abstract: Computer simulations offer a robust toolset for exploring complex systems
across various disciplines. A particularly impactful approach within this realm
is Agent-Based Modeling (ABM), which harnesses the interactions of individual
agents to emulate intricate system dynamics. ABM's strength lies in its
bottom-up methodology, illuminating emergent phenomena by modeling the
behaviors of individual components of a system. Yet, ABM has its own set of
challenges, notably its struggle with modeling natural language instructions
and common sense in mathematical equations or rules. This paper seeks to
transcend these boundaries by integrating Large Language Models (LLMs) like GPT
into ABM. This amalgamation gives birth to a novel framework, Smart Agent-Based
Modeling (SABM). Building upon the concept of smart agents -- entities
characterized by their intelligence, adaptability, and computation ability --
we explore in the direction of utilizing LLM-powered agents to simulate
real-world scenarios with increased nuance and realism. In this comprehensive
exploration, we elucidate the state of the art of ABM, introduce SABM's
potential and methodology, and present three case studies (source codes
available at https://github.com/Roihn/SABM), demonstrating the SABM methodology
and validating its effectiveness in modeling real-world systems. Furthermore,
we cast a vision towards several aspects of the future of SABM, anticipating a
broader horizon for its applications. Through this endeavor, we aspire to
redefine the boundaries of computer simulations, enabling a more profound
understanding of complex systems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective
Abstract: Adversarial Training (AT) has become arguably the state-of-the-art algorithm
for extracting robust features. However, researchers recently notice that AT
suffers from severe robust overfitting problems, particularly after learning
rate (LR) decay. In this paper, we explain this phenomenon by viewing
adversarial training as a dynamic minimax game between the model trainer and
the attacker. Specifically, we analyze how LR decay breaks the balance between
the minimax game by empowering the trainer with a stronger memorization
ability, and show such imbalance induces robust overfitting as a result of
memorizing non-robust features. We validate this understanding with extensive
experiments, and provide a holistic view of robust overfitting from the
dynamics of both the two game players. This understanding further inspires us
to alleviate robust overfitting by rebalancing the two players by either
regularizing the trainer's capacity or improving the attack strength.
Experiments show that the proposed ReBalanced Adversarial Training (ReBAT) can
attain good robustness and does not suffer from robust overfitting even after
very long training. Code is available at https://github.com/PKU-ML/ReBAT.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Out of Context: How important is Local Context in Neural Program Repair?
Abstract: Deep learning source code models have been applied very successfully to the
problem of automated program repair. One of the standing issues is the small
input window of current models which often cannot fully fit the context code
required for a bug fix (e.g., method or class declarations of a project).
Instead, input is often restricted to the local context, that is, the lines
below and above the bug location. In this work we study the importance of this
local context on repair success: how much local context is needed?; is context
before or after the bug location more important? how is local context tied to
the bug type? To answer these questions we train and evaluate Transformer
models in many different local context configurations on three datasets and two
programming languages. Our results indicate that overall repair success
increases with the size of the local context (albeit not for all bug types) and
confirm the common practice that roughly 50-60% of the input window should be
used for context leading the bug. Our results are not only relevant for
researchers working on Transformer-based APR tools but also for benchmark and
dataset creators who must decide what and how much context to include in their
datasets.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering
Abstract: Single-cell RNA sequencing (scRNA-seq) is important to transcriptomic
analysis of gene expression. Recently, deep learning has facilitated the
analysis of high-dimensional single-cell data. Unfortunately, deep learning
models may leak sensitive information about users. As a result, Differential
Privacy (DP) is increasingly used to protect privacy. However, existing DP
methods usually perturb whole neural networks to achieve differential privacy,
and hence result in great performance overheads. To address this challenge, in
this paper, we take advantage of the uniqueness of the autoencoder that it
outputs only the dimension-reduced vector in the middle of the network, and
design a Differentially Private Deep Contrastive Autoencoder Network (DP-DCAN)
by partial network perturbation for single-cell clustering. Since only partial
network is added with noise, the performance improvement is obvious and
twofold: one part of network is trained with less noise due to a bigger privacy
budget, and the other part is trained without any noise. Experimental results
of six datasets have verified that DP-DCAN is superior to the traditional DP
scheme with whole network perturbation. Moreover, DP-DCAN demonstrates strong
robustness to adversarial attacks. The code is available at
https://github.com/LFD-byte/DP-DCAN.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning
Abstract: Off-policy dynamic programming (DP) techniques such as $Q$-learning have
proven to be important in sequential decision-making problems. In the presence
of function approximation, however, these techniques often diverge due to the
absence of Bellman completeness in the function classes considered, a crucial
condition for the success of DP-based methods. In this paper, we show how
off-policy learning techniques based on return-conditioned supervised learning
(RCSL) are able to circumvent these challenges of Bellman completeness,
converging under significantly more relaxed assumptions inherited from
supervised learning. We prove there exists a natural environment in which if
one uses two-layer multilayer perceptron as the function approximator, the
layer width needs to grow linearly with the state space size to satisfy Bellman
completeness while a constant layer width is enough for RCSL. These findings
take a step towards explaining the superior empirical performance of RCSL
methods compared to DP-based methods in environments with near-optimal
datasets. Furthermore, in order to learn from sub-optimal datasets, we propose
a simple framework called MBRCSL, granting RCSL methods the ability of dynamic
programming to stitch together segments from distinct trajectories. MBRCSL
leverages learned dynamics models and forward sampling to accomplish trajectory
stitching while avoiding the need for Bellman completeness that plagues all
dynamic programming algorithms. We propose both theoretical analysis and
experimental evaluation to back these claims, outperforming state-of-the-art
model-free and model-based offline RL algorithms across several simulated
robotics problems.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Characteristic Guidance: Non-linear Correction for DDPM at Large Guidance Scale
Abstract: Popular guidance for denoising diffusion probabilistic model (DDPM) linearly
combines distinct conditional models together to provide enhanced control over
samples. However, this approach overlooks nonlinear effects that become
significant when guidance scale is large. To address this issue, we propose
characteristic guidance, a novel method that provides non-linear correction for
classifier-free guided DDPMs. Such correction forces the guided DDPMs to
respect the Fokker-Planck equation of their underlying diffusion process, in a
way that is first-principle, training-free, derivative-free, and compatible
with existing sampling methods. Experiments show that characteristic guidance
is robust to various applications, offers enhanced control over sample
generation, suppresses color and exposure issues even for latent space
sampling, and can handle physics problems such as the phase transitions.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation
Abstract: Knowledge distillation(KD) has demonstrated remarkable success across various
domains, but its application to medical imaging tasks, such as kidney and liver
tumor segmentation, has encountered challenges. Many existing KD methods are
not specifically tailored for these tasks. Moreover, prevalent KD methods often
lack a careful consideration of what and from where to distill knowledge from
the teacher to the student. This oversight may lead to issues like the
accumulation of training bias within shallower student layers, potentially
compromising the effectiveness of KD. To address these challenges, we propose
Hierarchical Layer-selective Feedback Distillation (HLFD). HLFD strategically
distills knowledge from a combination of middle layers to earlier layers and
transfers final layer knowledge to intermediate layers at both the feature and
pixel levels. This design allows the model to learn higher-quality
representations from earlier layers, resulting in a robust and compact student
model. Extensive quantitative evaluations reveal that HLFD outperforms existing
methods by a significant margin. For example, in the kidney segmentation task,
HLFD surpasses the student model (without KD) by over 10pp, significantly
improving its focus on tumor-specific features. From a qualitative standpoint,
the student model trained using HLFD excels at suppressing irrelevant
information and can focus sharply on tumor-specific details, which opens a new
pathway for more efficient and accurate diagnostic tools.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation
Abstract: Deep neural networks have shown exemplary performance on semantic scene
understanding tasks on source domains, but due to the absence of style
diversity during training, enhancing performance on unseen target domains using
only single source domain data remains a challenging task. Generation of
simulated data is a feasible alternative to retrieving large style-diverse
real-world datasets as it is a cumbersome and budget-intensive process.
However, the large domain-specific inconsistencies between simulated and
real-world data pose a significant generalization challenge in semantic
segmentation. In this work, to alleviate this problem, we propose a novel
MultiResolution Feature Perturbation (MRFP) technique to randomize
domain-specific fine-grained features and perturb style of coarse features. Our
experimental results on various urban-scene segmentation datasets clearly
indicate that, along with the perturbation of style-information, perturbation
of fine-feature components is paramount to learn domain invariant robust
feature maps for semantic segmentation models. MRFP is a simple and
computationally efficient, transferable module with no additional learnable
parameters or objective functions, that helps state-of-the-art deep neural
networks to learn robust domain invariant features for simulation-to-real
semantic segmentation.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Survey on Multimodal Large Language Models for Autonomous Driving
Abstract: With the emergence of Large Language Models (LLMs) and Vision Foundation
Models (VFMs), multimodal AI systems benefiting from large models have the
potential to equally perceive the real world, make decisions, and control tools
as humans. In recent months, LLMs have shown widespread attention in autonomous
driving and map systems. Despite its immense potential, there is still a lack
of a comprehensive understanding of key challenges, opportunities, and future
endeavors to apply in LLM driving systems. In this paper, we present a
systematic investigation in this field. We first introduce the background of
Multimodal Large Language Models (MLLMs), the multimodal models development
using LLMs, and the history of autonomous driving. Then, we overview existing
MLLM tools for driving, transportation, and map systems together with existing
datasets and benchmarks. Moreover, we summarized the works in The 1st WACV
Workshop on Large Language and Vision Models for Autonomous Driving (LLVM-AD),
which is the first workshop of its kind regarding LLMs in autonomous driving.
To further promote the development of this field, we also discuss several
important problems regarding using MLLMs in autonomous driving systems that
need to be solved by both academia and industry.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Artificial Intelligence in the automatic coding of interviews on Landscape Quality Objectives. Comparison and case study
Abstract: In this study, we conducted a comparative analysis of the automated coding
provided by three Artificial Intelligence functionalities (At-las.ti, ChatGPT
and Google Bard) in relation to the manual coding of 12 research interviews
focused on Landscape Quality Objectives for a small island in the north of Cuba
(Cayo Santa Mar\'ia). For this purpose, the following comparison criteria were
established: Accuracy, Comprehensiveness, Thematic Coherence, Redundancy,
Clarity, Detail and Regularity. The analysis showed the usefulness of AI for
the intended purpose, albeit with numerous flaws and shortcomings. In summary,
today the automatic coding of AIs can be considered useful as a guide towards a
subsequent in-depth and meticulous analysis of the information by the
researcher. However, as this is such a recently developed field, rapid
evolution is expected to bring the necessary improvements to these tools.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Diagnosis driven Anomaly Detection for CPS
Abstract: In Cyber-Physical Systems (CPS) research, anomaly detection (detecting
abnormal behavior) and diagnosis (identifying the underlying root cause) are
often treated as distinct, isolated tasks. However, diagnosis algorithms
require symptoms, i.e. temporally and spatially isolated anomalies, as input.
Thus, anomaly detection and diagnosis must be developed together to provide a
holistic solution for diagnosis in CPS. We therefore propose a method for
utilizing deep learning-based anomaly detection to generate inputs for
Consistency-Based Diagnosis (CBD). We evaluate our approach on a simulated and
a real-world CPS dataset, where our model demonstrates strong performance
relative to other state-of-the-art models.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: On Bringing Robots Home
Abstract: Throughout history, we have successfully integrated various machines into our
homes. Dishwashers, laundry machines, stand mixers, and robot vacuums are a few
recent examples. However, these machines excel at performing only a single task
effectively. The concept of a "generalist machine" in homes - a domestic
assistant that can adapt and learn from our needs, all while remaining
cost-effective - has long been a goal in robotics that has been steadily
pursued for decades. In this work, we initiate a large-scale effort towards
this goal by introducing Dobb-E, an affordable yet versatile general-purpose
system for learning robotic manipulation within household settings. Dobb-E can
learn a new task with only five minutes of a user showing it how to do it,
thanks to a demonstration collection tool ("The Stick") we built out of cheap
parts and iPhones. We use the Stick to collect 13 hours of data in 22 homes of
New York City, and train Home Pretrained Representations (HPR). Then, in a
novel home environment, with five minutes of demonstrations and fifteen minutes
of adapting the HPR model, we show that Dobb-E can reliably solve the task on
the Stretch, a mobile robot readily available on the market. Across roughly 30
days of experimentation in homes of New York City and surrounding areas, we
test our system in 10 homes, with a total of 109 tasks in different
environments, and finally achieve a success rate of 81%. Beyond success
percentages, our experiments reveal a plethora of unique challenges absent or
ignored in lab robotics. These range from effects of strong shadows, to
variable demonstration quality by non-expert users. With the hope of
accelerating research on home robots, and eventually seeing robot butlers in
every home, we open-source Dobb-E software stack and models, our data, and our
hardware designs at https://dobb-e.com

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: RAT: Reinforcement-Learning-Driven and Adaptive Testing for Vulnerability Discovery in Web Application Firewalls
Abstract: Due to the increasing sophistication of web attacks, Web Application
Firewalls (WAFs) have to be tested and updated regularly to resist the
relentless flow of web attacks. In practice, using a brute-force attack to
discover vulnerabilities is infeasible due to the wide variety of attack
patterns. Thus, various black-box testing techniques have been proposed in the
literature. However, these techniques suffer from low efficiency. This paper
presents Reinforcement-Learning-Driven and Adaptive Testing (RAT), an automated
black-box testing strategy to discover injection vulnerabilities in WAFs. In
particular, we focus on SQL injection and Cross-site Scripting, which have been
among the top ten vulnerabilities over the past decade. More specifically, RAT
clusters similar attack samples together. It then utilizes a reinforcement
learning technique combined with a novel adaptive search algorithm to discover
almost all bypassing attack patterns efficiently. We compare RAT with three
state-of-the-art methods considering their objectives. The experiments show
that RAT performs 33.53% and 63.16% on average better than its counterparts in
discovering the most possible bypassing payloads and reducing the number of
attempts before finding the first bypassing payload when testing
well-configured WAFs, respectively.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Stealthy and Persistent Unalignment on Large Language Models via Backdoor Injections
Abstract: Recent developments in Large Language Models (LLMs) have manifested
significant advancements. To facilitate safeguards against malicious
exploitation, a body of research has concentrated on aligning LLMs with human
preferences and inhibiting their generation of inappropriate content.
Unfortunately, such alignments are often vulnerable: fine-tuning with a minimal
amount of harmful data can easily unalign the target LLM. While being
effective, such fine-tuning-based unalignment approaches also have their own
limitations: (1) non-stealthiness, after fine-tuning, safety audits or
red-teaming can easily expose the potential weaknesses of the unaligned models,
thereby precluding their release/use. (2) non-persistence, the unaligned LLMs
can be easily repaired through re-alignment, i.e., fine-tuning again with
aligned data points. In this work, we show that it is possible to conduct
stealthy and persistent unalignment on large language models via backdoor
injections. We also provide a novel understanding on the relationship between
the backdoor persistence and the activation pattern and further provide
guidelines for potential trigger design. Through extensive experiments, we
demonstrate that our proposed stealthy and persistent unalignment can
successfully pass the safety evaluation while maintaining strong persistence
against re-alignment defense.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: GREEMA: Proposal and Experimental Verification of Growing Robot by Eating Environmental MAterial for Landslide Disaster
Abstract: In areas that are inaccessible to humans, such as the lunar surface and
landslide sites, there is a need for multiple autonomous mobile robot systems
that can replace human workers. In particular, at landslide sites such as river
channel blockages, robots are required to remove water and sediment from the
site as soon as possible. Conventionally, several construction machines have
been deployed to the site for civil engineering work. However, because of the
large size and weight of conventional construction equipment, it is difficult
to move multiple units of construction equipment to the site, resulting in
significant transportation costs and time. To solve such problems, this study
proposes a novel growing robot by eating environmental material called GREEMA,
which is lightweight and compact during transportation, but can function by
eating on environmental materials once it arrives at the site. GREEMA actively
takes in environmental materials such as water and sediment, uses them as its
structure, and removes them by moving itself. In this paper, we developed and
experimentally verified two types of GREEMAs. First, we developed a fin-type
swimming robot that passively takes water into its body using a water-absorbing
polymer and forms a body to express its swimming function. Second, we
constructed an arm-type robot that eats soil to increase the rigidity of its
body. We discuss the results of these two experiments from the viewpoint of
Explicit-Implicit control and describe the design theory of GREEMA.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Aspects of human memory and Large Language Models
Abstract: Large Language Models (LLMs) are huge artificial neural networks which
primarily serve to generate text, but also provide a very sophisticated
probabilistic model of language use. Since generating a semantically consistent
text requires a form of effective memory, we investigate the memory properties
of LLMs and find surprising similarities with key characteristics of human
memory. We argue that the human-like memory properties of the Large Language
Model do not follow automatically from the LLM architecture but are rather
learned from the statistics of the training textual data. These results
strongly suggest that the biological features of human memory leave an imprint
on the way that we structure our textual narratives.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Games
Abstract: We explore the generation of diverse environments using the Amorphous
Fortress (AF) simulation framework. AF defines a set of Finite State Machine
(FSM) nodes and edges that can be recombined to control the behavior of agents
in the `fortress' grid-world. The behaviors and conditions of the agents within
the framework are designed to capture the common building blocks of multi-agent
artificial life and reinforcement learning environments. Using quality
diversity evolutionary search, we generate diverse sets of environments. These
environments exhibit certain types of complexity according to measures of
agents' FSM architectures and activations, and collective behaviors. Our
approach, Quality Diversity in Amorphous Fortress (QD-AF) generates families of
0-player games akin to simplistic ecological models, and we identify the
emergence of both competitive and co-operative multi-agent and multi-species
survival dynamics. We argue that these generated worlds can collectively serve
as training and testing grounds for learning algorithms.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
Abstract: Large Language Models (LLMs) have gained significant popularity for their
impressive performance across diverse fields. However, LLMs are prone to
hallucinate untruthful or nonsensical outputs that fail to meet user
expectations in many real-world applications. Existing works for detecting
hallucinations in LLMs either rely on external knowledge for reference
retrieval or require sampling multiple responses from the LLM for consistency
verification, making these methods costly and inefficient. In this paper, we
propose a novel reference-free, uncertainty-based method for detecting
hallucinations in LLMs. Our approach imitates human focus in factuality
checking from three aspects: 1) focus on the most informative and important
keywords in the given text; 2) focus on the unreliable tokens in historical
context which may lead to a cascade of hallucinations; and 3) focus on the
token properties such as token type and token frequency. Experimental results
on relevant datasets demonstrate the effectiveness of our proposed method,
which achieves state-of-the-art performance across all the evaluation metrics
and eliminates the need for additional information.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: A Security Risk Taxonomy for Large Language Models
Abstract: As large language models (LLMs) permeate more and more applications, an
assessment of their associated security risks becomes increasingly necessary.
The potential for exploitation by malicious actors, ranging from disinformation
to data breaches and reputation damage, is substantial. This paper addresses a
gap in current research by focusing on the security risks posed by LLMs, which
extends beyond the widely covered ethical and societal implications. Our work
proposes a taxonomy of security risks along the user-model communication
pipeline, explicitly focusing on prompt-based attacks on LLMs. We categorize
the attacks by target and attack type within a prompt-based interaction scheme.
The taxonomy is reinforced with specific attack examples to showcase the
real-world impact of these risks. Through this taxonomy, we aim to inform the
development of robust and secure LLM applications, enhancing their safety and
trustworthiness.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Agent-OM: Leveraging Large Language Models for Ontology Matching
Abstract: Ontology matching (OM) enables semantic interoperability between different
ontologies and resolves their conceptual heterogeneity by aligning related
entities. OM systems currently have two prevailing design paradigms:
conventional knowledge-based expert systems and newer machine learning-based
predictive systems. While large language models (LLMs) and LLM-based agents
have become revolutionary in data engineering and have been applied creatively
in various domains, their potential for OM remains underexplored. This study
introduces a novel agent-powered LLM-based design paradigm for OM systems. With
thoughtful consideration of several specific challenges to leverage LLMs for
OM, we propose a generic framework, namely Agent-OM, consisting of two Siamese
agents for retrieval and matching, with a set of simple prompt-based OM tools.
Our framework is implemented in a proof-of-concept system. Evaluations of three
Ontology Alignment Evaluation Initiative (OAEI) tracks over state-of-the-art OM
systems show that our system can achieve very close results to the best
long-standing performance on simple OM tasks and significantly improve the
performance on complex and few-shot OM tasks.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions
Abstract: Conversational question-answering (CQA) systems aim to create interactive
search systems that effectively retrieve information by interacting with users.
To replicate human-to-human conversations, existing work uses human annotators
to play the roles of the questioner (student) and the answerer (teacher).
Despite its effectiveness, challenges exist as human annotation is
time-consuming, inconsistent, and not scalable. To address this issue and
investigate the applicability of large language models (LLMs) in CQA
simulation, we propose a simulation framework that employs zero-shot learner
LLMs for simulating teacher-student interactions. Our framework involves two
LLMs interacting on a specific topic, with the first LLM acting as a student,
generating questions to explore a given search topic. The second LLM plays the
role of a teacher by answering questions and is equipped with additional
information, including a text on the given topic. We implement both the student
and teacher by zero-shot prompting the GPT-4 model. To assess the effectiveness
of LLMs in simulating CQA interactions and understand the disparities between
LLM- and human-generated conversations, we evaluate the simulated data from
various perspectives. We begin by evaluating the teacher's performance through
both automatic and human assessment. Next, we evaluate the performance of the
student, analyzing and comparing the disparities between questions generated by
the LLM and those generated by humans. Furthermore, we conduct extensive
analyses to thoroughly examine the LLM performance by benchmarking
state-of-the-art reading comprehension models on both datasets. Our results
reveal that the teacher LLM generates lengthier answers that tend to be more
accurate and complete. The student LLM generates more diverse questions,
covering more aspects of a given topic.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Sum-of-Parts Models: Faithful Attributions for Groups of Features
Abstract: An explanation of a machine learning model is considered "faithful" if it
accurately reflects the model's decision-making process. However, explanations
such as feature attributions for deep learning are not guaranteed to be
faithful, and can produce potentially misleading interpretations. In this work,
we develop Sum-of-Parts (SOP), a class of models whose predictions come with
grouped feature attributions that are faithful-by-construction. This model
decomposes a prediction into an interpretable sum of scores, each of which is
directly attributable to a sparse group of features. We evaluate SOP on
benchmarks with standard interpretability metrics, and in a case study, we use
the faithful explanations from SOP to help astrophysicists discover new
knowledge about galaxy formation.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Separating and Learning Latent Confounders to Enhancing User Preferences Modeling
Abstract: Recommender models aim to capture user preferences from historical feedback
and then predict user-specific feedback on candidate items. However, the
presence of various unmeasured confounders causes deviations between the user
preferences in the historical feedback and the true preferences, resulting in
models not meeting their expected performance. Existing debias models either
(1) specific to solving one particular bias or (2) directly obtain auxiliary
information from user historical feedback, which cannot identify whether the
learned preferences are true user preferences or mixed with unmeasured
confounders. Moreover, we find that the former recommender system is not only a
successor to unmeasured confounders but also acts as an unmeasured confounder
affecting user preference modeling, which has always been neglected in previous
studies. To this end, we incorporate the effect of the former recommender
system and treat it as a proxy for all unmeasured confounders. We propose a
novel framework, \textbf{S}eparating and \textbf{L}earning Latent Confounders
\textbf{F}or \textbf{R}ecommendation (\textbf{SLFR}), which obtains the
representation of unmeasured confounders to identify the counterfactual
feedback by disentangling user preferences and unmeasured confounders, then
guides the target model to capture the true preferences of users. Extensive
experiments in five real-world datasets validate the advantages of our method.

Output: Information Retrieval