Datasets:

madrylab
/

platinum-bench

	---
	license: cc-by-sa-4.0
	configs:
	- config_name: bbh_logical_deduction_three_objects
	data_files:
	- split: test
	path: bbh_logical_deduction_three_objects/test-*
	- config_name: bbh_navigate
	data_files:
	- split: test
	path: bbh_navigate/test-*
	- config_name: bbh_object_counting
	data_files:
	- split: test
	path: bbh_object_counting/test-*
	- config_name: drop
	data_files:
	- split: test
	path: drop/test-*
	- config_name: gsm8k
	data_files:
	- split: test
	path: gsm8k/test-*
	- config_name: hotpotqa
	data_files:
	- split: test
	path: hotpotqa/test-*
	- config_name: mmlu_math
	data_files:
	- split: test
	path: mmlu_math/test-*
	- config_name: multiarith
	data_files:
	- split: test
	path: multiarith/test-*
	- config_name: singleop
	data_files:
	- split: test
	path: singleop/test-*
	- config_name: singleq
	data_files:
	- split: test
	path: singleq/test-*
	- config_name: squad
	data_files:
	- split: test
	path: squad/test-*
	- config_name: svamp
	data_files:
	- split: test
	path: svamp/test-*
	- config_name: tab_fact
	data_files:
	- split: test
	path: tab_fact/test-*
	- config_name: vqa
	data_files:
	- split: test
	path: vqa/test-*
	- config_name: winograd_wsc
	data_files:
	- split: test
	path: winograd_wsc/test-*
	dataset_info:
	- config_name: bbh_logical_deduction_three_objects
	features:
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	dtype: string
	- name: platinum_prompt_no_cot
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	- name: platinum_parsing_strategy
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	- name: target
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	- name: answer
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	- name: type
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	- name: level
	dtype: string
	- name: supporting_facts
	struct:
	- name: sent_id
	sequence: int64
	- name: title
	sequence: string
	- name: context
	struct:
	- name: sentences
	sequence:
	sequence: string
	- name: title
	sequence: string
	splits:
	- name: test
	num_bytes: 2164891
	num_examples: 250
	download_size: 1288500
	dataset_size: 2164891
	- config_name: mmlu_math
	features:
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	- name: platinum_prompt
	dtype: string
	- name: platinum_prompt_no_cot
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	- name: answer
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	num_examples: 270
	download_size: 113723
	dataset_size: 287212
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	features:
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	- name: platinum_prompt
	dtype: string
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	dtype: string
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	num_examples: 174
	download_size: 54214
	dataset_size: 157371
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	features:
	- name: cleaning_status
	dtype: string
	- name: platinum_prompt
	dtype: string
	- name: platinum_prompt_no_cot
	dtype: string
	- name: platinum_target
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	- name: input
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	dtype: string
	- name: dataset
	dtype: string
	splits:
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	num_examples: 159
	download_size: 45006
	dataset_size: 118922
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	- name: platinum_prompt
	dtype: string
	- name: platinum_prompt_no_cot
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	dtype: string
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	dtype: string
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	dtype: string
	- name: output_answer
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	- name: dataset
	dtype: string
	splits:
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	num_examples: 109
	download_size: 39915
	dataset_size: 96097
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	features:
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	- name: context
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	- name: question
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	- name: text
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	- name: Type
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	dtype: string
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	- name: platinum_prompt
	dtype: string
	- name: platinum_prompt_no_cot
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	- name: platinum_target
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	- name: pronoun_loc
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	- name: label
	dtype: int64
	- name: source
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	splits:
	- name: test
	num_bytes: 198631
	num_examples: 200
	download_size: 54931
	dataset_size: 198631
	task_categories:
	- question-answering
	language:
	- en
	---

	# Dataset Card for PlatinumBench

	[🏆 Leaderboard](http://platinum-bench.csail.mit.edu/)  \|  [🖥️ Code](https://github.com/MadryLab/platinum-benchmarks/)  \|  [📖 Paper](https://arxiv.org/abs/2502.03461)

	## 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](mailto:[email protected]), [Edward Vendrow](mailto:[email protected])

	### 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:

	```python
	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](https://visualqa.org/download.html), 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:
	1. concensus: all LLMs agreed with the label, so the example was not manually reviewed (`platinum_target` == `original_target` by default).
	2. verified: the original target was maually verified to be correct (`platinum_target` == `original_target`).
	3. revised: the label is updated from the original label (`platinum_target` != `original_target`).
	4. rejected: the example is removed due to issues such as ambiguity.
	- 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 like `o1` that 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.

	> [!NOTE]
	> 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:


	\| \| Type \| URL \| Subset \| Split \| License
	\| ----- \| ------ \| ----- \| ---- \| ----\| ----\|
	\| SingleOp \| Math \| https://huggingface.co/datasets/allenai/lila \| singleop \| test \| [CC BY 4.0](https://github.com/allenai/Lila/blob/main/LICENSE.txt)
	\| SingleEq \| Math \| https://huggingface.co/datasets/allenai/lila \| singleeq \| test \| [CC BY 4.0](https://github.com/allenai/Lila/blob/main/LICENSE.txt)
	\| MultiArith \| Math \| https://huggingface.co/datasets/allenai/lila \| multiarith \| test \| [CC BY 4.0](https://github.com/allenai/Lila/blob/main/LICENSE.txt)
	\| SVAMP \| Math \| https://huggingface.co/datasets/ChilleD/svamp \| default \| test \| [MIT](https://github.com/arkilpatel/SVAMP/blob/main/LICENSE)
	\| GSM8K \| Math \| https://huggingface.co/datasets/openai/gsm8k \| main \| test \| [MIT](https://github.com/openai/grade-school-math/blob/master/LICENSE)
	\| MMLU High‑School Math \| Math \| https://huggingface.co/datasets/cais/mmlu \| high_school_mathematics \| test \| [MIT](https://github.com/hendrycks/test/blob/master/LICENSE)
	\| Logic. Ded. 3-Obj \| Logic \| https://huggingface.co/datasets/maveriq/bigbenchhard \| logical_deduction_three_objects \| train \| [MIT](https://github.com/suzgunmirac/BIG-Bench-Hard/blob/main/LICENSE)
	\| Object Counting \| Logic \| https://huggingface.co/datasets/maveriq/bigbenchhard \| object_counting \| train \| [MIT](https://github.com/suzgunmirac/BIG-Bench-Hard/blob/main/LICENSE)
	\| Navigate \| Logic \| https://huggingface.co/datasets/maveriq/bigbenchhard \| navigate \| train \| [MIT](https://github.com/suzgunmirac/BIG-Bench-Hard/blob/main/LICENSE)
	\| TabFact \| Table Understanding \| https://huggingface.co/datasets/wenhu/tab_fact \| tab_fact \| test \| [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)
	\| HotPotQA \| Reading Comp. \| https://huggingface.co/datasets/hotpotqa/hotpot_qa \| distractor \| validation \| [CC BY‑SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
	\| SQuAD2.0 \| Reading Comp. \| https://huggingface.co/datasets/rajpurkar/squad_v2 \| squad_v2 \| validation \| [CC BY‑SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
	\| DROP \| Reading Comp. \| https://huggingface.co/datasets/ucinlp/drop \| default \| validation \| [CC BY‑SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
	\| Wingograd WSC \| Commonsense \| https://huggingface.co/datasets/ErnestSDavis/winograd_wsc \| wsc285 \| test \| [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)
	\| VQA \| Vision \| https://visualqa.org/download.html \| N/A \| validation \| [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)

	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](https://creativecommons.org/licenses/by-sa/4.0/legalcode) license.

	### Citation Information
	Cite this dataset and the source datasets (see [sources.bib](https://github.com/MadryLab/platinum-benchmarks/blob/main/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},
	}
	```