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---
dataset_info:
- config_name: finance
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configs:
- config_name: finance
data_files:
- split: validation_pref
path: finance/validation_pref-*
- split: test_pref
path: finance/test_pref-*
- split: train_pref
path: finance/train_pref-*
- config_name: finance_test_prompts
data_files:
- split: test_prompts
path: finance_test_prompts/test_prompts-*
- config_name: gender_sexuality
data_files:
- split: validation_pref
path: gender_sexuality/validation_pref-*
- split: test_pref
path: gender_sexuality/test_pref-*
- split: train_pref
path: gender_sexuality/train_pref-*
- config_name: gender_sexuality_test_prompts
data_files:
- split: test_prompts
path: gender_sexuality_test_prompts/test_prompts-*
- config_name: history
data_files:
- split: validation_pref
path: history/validation_pref-*
- split: test_pref
path: history/test_pref-*
- split: train_pref
path: history/train_pref-*
- config_name: history_test_prompts
data_files:
- split: test_prompts
path: history_test_prompts/test_prompts-*
- config_name: politics
data_files:
- split: validation_pref
path: politics/validation_pref-*
- split: test_pref
path: politics/test_pref-*
- split: train_pref
path: politics/train_pref-*
- config_name: politics_test_prompts
data_files:
- split: test_prompts
path: politics_test_prompts/test_prompts-*
- config_name: science
data_files:
- split: validation_pref
path: science/validation_pref-*
- split: test_pref
path: science/test_pref-*
- split: train_pref
path: science/train_pref-*
- config_name: science_test_prompts
data_files:
- split: test_prompts
path: science_test_prompts/test_prompts-*
license: odc-by
language:
- en
pretty_name: c
---
# Dataset Card for "compred"
## Dataset Details
### Dataset Description
Conventional algorithms for training language models (LMs) with human feedback rely on preferences that are assumed to account for an average user,
disregarding subjectivity and finer-grained variations. Recent studies have raised concerns about aggregating such diverse, often contradictory human feedback to train
a single universal reward model, questioning which values or voices the models align with.
Finetuning models to maximize such reward results in generic models that produce outputs not preferred by many user groups,
as they tend to average out styles and norms. To study this issue, we collect and release **ComPRed**,
a question-answering dataset with community-level preferences from Reddit.
This dataset facilitates studying diversity in preferences without incurring privacy concerns associated with individual feedback.
- **Curated by:** Allen Institute for AI, The Ohio State University, Carnegie Mellon University
- **Paper:** [ArXiv](#)
- **Repository:** [https://github.com/allenai/compred](https://github.com/allenai/compred)
- **Language(s) (NLP):** English
- **License:** https://opendatacommons.org/licenses/by/odc_by_1.0_public_text.txt
- **Point of Contact:** [Sachin Kumar & Chan Young Park](mailto:[email protected], [email protected])
## Uses
ComPreD contains five subsets divided based on factors driving diverging user preferences (we followed a similar process as [SHP](https://huggingface.co/datasets/stanfordnlp/SHP) to create this dataset).
| Subset(s) | Factor |
| -------- | ------- |
| politics | Ideologies |
| gender_and_sexuality | Demographics |
| finance, history | Community Norms |
| science | Level of expertise / Community Norms |
### Loading
```python
from datasets import load_dataset
# load finance train set
finance_train_pref = load_dataset("allenai/compred", "finance", split="train_pref")
# load finance test prompts
finance_test_prompts = load_dataset("allenai/compred", "finance_test_prompts", split="test_prompts")
```
### Dataset Structure
Coming soon
<!-- Preference data in each subset (train_pref, validation_pref, test_pref) has the following fields:
- id (str): a unique identifier
- prompt (`str`): the instruction/query which can be safely complied with
- chosen (`str`): the compliant response from a stronger model
- chosen_model (`str`): gpt-4
- rejected (`str`): the noncompliant response from a weaker model
- rejected_model (`str`): where applicable
Test prompts in each subset (test_prompts) has the following fields:
- id (str): a unique identifier
- prompt (str): the instruction/query which should NOT be complied with (original set) or should be complied with (contrast)
- response (str): the noncompliant or compliant response (only in train split)
- category (str): a high-level noncompliance category defined in our taxonomy including: "incomplete requests", "unsupported requests", "indeterminate requests", "humanizing requests", and "requests with safety concerns"
- subcategory (str): a fine-grained subcategory under each category
-->
### Data Creation
Please refer to our [paper](#) for details on our dataset collection.
## Licensing Information
ComPreD is made available under the ODC-BY requiring the user to follow the licenses of the subsequent parts.
## Citation
```
@article{kumar-park2024,
title={{Personalized LMs: Aligning Language Models with Diverse Human Preferences}},
author={Sachin Kumar, Chan Young Park, Yulia Tsvetkov, Noah A. Smith, Hannaneh Hajishirzi},
journal={},
year={2024}
}
```