Datasets:
metadata
license: cc-by-sa-4.0
configs:
- config_name: bbh_logical_deduction_three_objects
data_files:
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path: bbh_logical_deduction_three_objects/test-*
- config_name: bbh_navigate
data_files:
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path: bbh_navigate/test-*
- config_name: bbh_object_counting
data_files:
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- config_name: drop
data_files:
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path: drop/test-*
- config_name: gsm8k
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- config_name: hotpotqa
data_files:
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- config_name: mmlu_math
data_files:
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- config_name: multiarith
data_files:
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path: multiarith/test-*
- config_name: singleop
data_files:
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path: singleop/test-*
- config_name: singleq
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- config_name: squad
data_files:
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path: squad/test-*
- config_name: svamp
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- config_name: vqa
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path: vqa/test-*
- config_name: winograd_wsc
data_files:
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dataset_info:
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task_categories:
- question-answering
language:
- en
Dataset Card for PlatinumBench
🏆 Leaderboard | 🖥️ Code | 📖 Paper
Dataset Description
- Homepage: http://platinum-bench.csail.mit.edu/
- Repository: https://github.com/MadryLab/platinum-benchmarks/
- Paper: https://arxiv.org/abs/2502.03461
- Leaderboard: http://platinum-bench.csail.mit.edu/
- Point of Contact: Joshua Vendrow, Edward Vendrow
Dataset Summary
Platinum Benchmarks are benchmarks that are are carefully curated to minimize label errors and ambiguity, allowing us to measure reliability of models.
This dataset containts fifteen platinum benchmarks created by manually revising questions from existing datasets (see the github repo for details on accessing our revised subset of VQA). To revise each benchmark, we ran a vareity of frontier models on individual examples and manually re-annotated any example for which at least one model made an error. See the paper for further details on the revision process.
Load the Dataset
To load the dataset using HuggingFace datasets, you first need to pip install datasets, then run the following code:
from datasets import load_dataset
ds = load_dataset("madrylab/platinum-bench", name="gsm8k", split="test") # or another subset
ds = ds.filter(lambda x: x['cleaning_status'] != 'rejected') # filter out rejected questions
Dataset structure
Data Instances
We accessed each of the fourteen original natural language benchmarks that we revised from their respective huggingface repositories, and each benchmark had its own per-instance data fields/columns. We have standardized these benchmarks by providing pre-constructed prompts for each dataset (under 'platinum_prompt'). Each prompt template automatically formats the relevant dataset columns into a consistent structure. You can use these standardized prompts directly, but we include the original dataset columns for those interested in their own prompting, or to seamlessly subtitute our revised benchmarks for the original versions.
For VQA, we source images and annotataions from their official website, and reference images by their image path in the original downloaded directory format (see our GitHub repository for additional details).
An example from the PlatinumBench GSM8K subset looks as follows:
{'cleaning_status': 'consensus',
'platinum_prompt': 'Solve the following math word problem.\n\nA robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?\n\nThink step-by-step. Then, provide the final answer as a single integer in the format "Answer: XXX" with no extra formatting.',
'platinum_prompt_no_cot': 'Solve the following math word problem.\n\nA robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?\n\nThen, provide the final answer as a single integer in the format "Answer: XXX" with no extra formatting.',
'platinum_target': ['3'],
'platinum_parsing_strategy': 'math',
'original_target': ['3']
'question': 'A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?',
'answer': 'It takes 2/2=<<2/2=1>>1 bolt of white fiber\nSo the total amount of fabric is 2+1=<<2+1=3>>3 bolts of fabric\n#### 3'}
Data Fields
- cleaning_status (
str): One of:- concensus: all LLMs agreed with the label, so the example was not manually reviewed (
platinum_target==original_targetby default). - verified: the original target was maually verified to be correct (
platinum_target==original_target). - revised: the label is updated from the original label (
platinum_target!=original_target). - rejected: the example is removed due to issues such as ambiguity.
- concensus: all LLMs agreed with the label, so the example was not manually reviewed (
- platinum_prompt (
str): A chain-of-thought question prompt that can be directly asked to a language model. This is constructed from fields in the original dataset. - platinum_prompt_no_cot (
str): The same prompt, but without explicity chain-of-thought instructions. This is used for models likeo1that don't need chain-of-thought prompting. - platinum_target (
List[str]): The list of all correct answers for the question. In most cases there is just one correct answer. - original_target (
str): The original target provided in the dataset. This is can be different from the platinum target if it is incorrect. - platinum_parsing_strategy (
str): The parser that should be used to parse the LLM answer. Refer to the provided code. - image_path (
str): Only included for VQA. The image path from which to source the relevant image, such as:'val2014/COCO_val2014_000000304481.jpg. - We also incude all the original dataset columns after these ones.
This HuggingFace dataset includes rejected questions that are not used for evaluation. To use only questions that we include in our platinum benchmarks, make sure to filter these out:
ds = ds.filter(lambda x: x['cleaning_status'] != 'rejected')
Prompt Example
Here is an example of the standardized prompt we provide for a question from MultiArith:
Solve the following math word problem.
At the schools book fair Sam bought 13 adventure books and 17 mystery books. If 15 of the books were used, how many new books did he buy?
Think step-by-step. Then, provide the final answer as a single number in the format "Answer: XXX" with no extra formatting.
The specific prompt template and parsing strategy depends on the model, although many of them are common between datasets.
Dataset Creation
Curation Rationale
Many current LLM benchmarks are riddled with label noise such as mislabeled or ambiguous questions. Due to this label noise, progress in these benchmarks often stalls before models actually achieve reliable performance on them. As a result, the comminuty often considers these benchmarks to be "saturated" and discards them too early, discouraging machine learning practictioners from ever striving to achieve proper reliability. As a first step towards addressing this gap in benchmarking practices, we revise samples from fifteen "saturated" benchmark to minimize label noise.
Source Data and Attribution
Each of the fifteen benchmarks that we revise was sourced from the following huggingface repositories:
Please defer to the datasets cards of these benchmarks for further details on their collection and annotation process.
Additional Information
Licensing Information
See the table above for the licensing information of the original datasets upon which our work is based. The further annotations we provide are licensed under the CC BY-SA 4.0 license.
Citation Information
Cite this dataset and the source datasets (see sources.bib).
@misc{vendrow2025largelanguagemodelbenchmarks,
title={Do Large Language Model Benchmarks Test Reliability?},
author={Joshua Vendrow and Edward Vendrow and Sara Beery and Aleksander Madry},
year={2025},
eprint={2502.03461},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2502.03461},
}