--- 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: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 305159 num_examples: 200 download_size: 60084 dataset_size: 305159 - config_name: bbh_navigate features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 166521 num_examples: 200 download_size: 29525 dataset_size: 166521 - config_name: bbh_object_counting features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 128265 num_examples: 200 download_size: 31211 dataset_size: 128265 - config_name: drop features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: section_id dtype: string - name: query_id dtype: string - name: passage dtype: string - name: question dtype: string - name: answers_spans struct: - name: spans sequence: string - name: types sequence: string splits: - name: test num_bytes: 957113 num_examples: 250 download_size: 469801 dataset_size: 957113 - config_name: gsm8k features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 411558 num_examples: 300 download_size: 200727 dataset_size: 411558 - config_name: hotpotqa features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: type dtype: string - 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: 2163497 num_examples: 250 download_size: 1287407 dataset_size: 2163497 - config_name: mmlu_math features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 287231 num_examples: 270 download_size: 113739 dataset_size: 287231 - config_name: multiarith features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: input dtype: string - name: output_program dtype: string - name: output_answer dtype: string - name: split dtype: string - name: dataset dtype: string splits: - name: test num_bytes: 157371 num_examples: 174 download_size: 54214 dataset_size: 157371 - config_name: singleop features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: input dtype: string - name: output_program dtype: string - name: output_answer dtype: string - name: split dtype: string - name: dataset dtype: string splits: - name: test num_bytes: 118922 num_examples: 159 download_size: 45006 dataset_size: 118922 - config_name: singleq features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: input dtype: string - name: output_program dtype: string - name: output_answer dtype: string - name: split dtype: string - name: dataset dtype: string splits: - name: test num_bytes: 96097 num_examples: 109 download_size: 39915 dataset_size: 96097 - config_name: squad features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: test num_bytes: 860040 num_examples: 250 download_size: 464857 dataset_size: 860040 - config_name: svamp features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: ID dtype: string - name: Body dtype: string - name: Question dtype: string - name: Equation dtype: string - name: Answer dtype: string - name: Type dtype: string - name: question_concat dtype: string splits: - name: test num_bytes: 322658 num_examples: 300 download_size: 116772 dataset_size: 322658 - config_name: tab_fact features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: id dtype: int64 - name: table_id dtype: string - name: table_text dtype: string - name: table_caption dtype: string - name: statement dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 1137041 num_examples: 200 download_size: 475116 dataset_size: 1137041 - config_name: vqa features: - name: cleaning_status dtype: string - name: image_path dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: 'null' - name: platinum_parsing_stratagy dtype: string - name: question_type dtype: string - name: multiple_choice_answer dtype: string - name: answers list: - name: answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: image_id dtype: int64 - name: answer_type dtype: string - name: question_id dtype: int64 - name: question dtype: string splits: - name: test num_bytes: 122801 num_examples: 242 download_size: 26070 dataset_size: 122801 - config_name: winograd_wsc features: - name: cleaning_status dtype: string - name: platinum_prompt dtype: string - name: platinum_prompt_no_cot dtype: string - name: platinum_target sequence: string - name: original_target sequence: string - name: platinum_parsing_strategy dtype: string - name: text dtype: string - name: pronoun dtype: string - name: pronoun_loc dtype: int64 - name: quote dtype: string - name: quote_loc dtype: int64 - name: options sequence: string - name: label dtype: int64 - name: source dtype: string splits: - name: test num_bytes: 198631 num_examples: 200 download_size: 54961 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)  |  [**🔍 Error Viewer**](http://platinum-bench.csail.mit.edu/inspect) ## 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:jvendrow@mit.edu), [Edward Vendrow](mailto:evendrow@mit.edu) ### 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 contains 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 variety 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 ### Dataset Subsets & Cleaning Statistics Below we list each of the platinum benchmarks with the number of examples in each benchmark that we kept via consensus, revised, verified, or rejected. See "Data Fields" for a description of what each cleaning status means. | | Included | | | | Excluded | | ----- | ----- | ----- | ----- | ----- | ----- | Dataset | **# Included** | Consensus | Revised | Verified | Rejected SingleOp (Platinum) | **150** | 142 | 0 | 8 | 9 SingleEq (Platinum) | **100** | 87 | 0 | 13 | 9 MultiArith (Platinum) | **171** | 165 | 3 | 3 | 3 SVAMP (Platinum) | **268** | 222 | 3 | 43 | 32 GSM8K (Platinum) | **271** | 227 | 1 | 43 | 29 MMLU High‑School Math (Platinum) | **268** | 106 | 0 | 162 | 2 Logic. Ded. 3-Obj (Platinum) | **200** | 199 | 0 | 1 | 0 Object Counting (Platinum) | **190** | 58 | 0 | 132 | 10 Navigate (Platinum) | **200** | 134 | 0 | 66 | 0 TabFact (Platinum) | **173** | 58 | 3 | 112 | 27 HotPotQA (Platinum) | **183** | 48 | 89 | 46 | 67 SQUAD2.0 (Platinum) | **164** | 78 | 43 | 43 | 86 DROP (Platinum) | **209** | 30 | 177 | 2 | 41 Winograd WSC (Platinum) | **195** | 77 | 0 | 118 | 5 VQA (Platinum) | **242** | 0 | 242 | 0 | 358 ### 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}, } ```