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--- |
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dataset_info: |
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features: |
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- name: prompt |
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dtype: string |
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- name: metadata |
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dtype: string |
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- name: task |
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dtype: string |
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- name: dataset |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 432885480 |
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num_examples: 30647 |
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download_size: 132698519 |
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dataset_size: 432885480 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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task_categories: |
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- table-question-answering |
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--- |
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# Dataset Card for MMTU |
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## Dataset Summary |
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MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark |
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by Junjie Xing, [Yeye He](https://www.microsoft.com/en-us/research/people/yeyehe/), Mengyu Zhou, Haoyu Dong, Shi Han, Lingjiao Chen, Dongmei Zhang, [Surajit Chaudhuri](https://www.microsoft.com/en-us/research/people/surajitc/), and [H. V. Jagadish](https://web.eecs.umich.edu/~jag/). |
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[Paper](https://huggingface.co/papers/2506.05587) |
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[Code](https://github.com/MMTU-Benchmark/MMTU) |
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This is a large-scale benchmark designed to evaluate the table reasoning capabilities of large language models (LLMs). It consists of over 30,000 questions across 25 real-world table tasks, focusing on deep understanding, reasoning, and manipulation of tabular data. |
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These tasks are curated from decades of computer science research and represent challenges encountered by expert users in real applications, making MMTU a rigorous test for LLMs aspiring to professional-level table understanding. |
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A complete list of tasks: 'table-transform-by-relationalization', 'table-transform-by-output-schema', 'table-transform-by-output-table', 'Entity matching', 'Schema matching', 'Head value matching', 'data-imputation', 'error-detection', 'list-to-table', 'semantic-join', 'equi-join-detect', 'program-transform-by-example', 'formula-by-context', 'semantic-transform-by-example', 'arithmetic-relationship', 'functional-relationship', 'string-relationship', 'Needle-in-a-haystack-table', 'Needle-in-a-haystack-index', 'NL-2-SQL', 'Table Question Answering', 'Fact Verification', 'Column type annotation', 'Column property annotation', 'Cell entity annotation'. |
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## Leaderboards |
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| **Model Type** | **Model** | **MMTU Score** | |
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|----------------|---------------------|----------------------| |
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| Reasoning | o4-mini | **0.637 ± 0.01** | |
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| Reasoning | Deepseek-R1 | 0.557 ± 0.01 | |
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| Chat | Deepseek-V3 | 0.517 ± 0.01 | |
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| Chat | GPT-4o | 0.490 ± 0.01 | |
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| Chat | Llama-3.3-70B | 0.438 ± 0.01 | |
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| Chat | Mistral-Large | 0.430 ± 0.01 | |
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| Chat | Mistral-Small | 0.402 ± 0.01 | |
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| Chat | GPT-4o-mini | 0.386 ± 0.01 | |
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| Chat | Llama-3.1-8B | 0.259 ± 0.01 | |
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## Language |
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English |
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## Data Structure |
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### Data Fields |
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- prompt: The prompt presented in the MMTU instance. |
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- metadata: Supplementary information associated with the MMTU instance, typically used for evaluation purposes. |
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- task: The specific subtask category within the MMTU framework to which the instance belongs. |
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- dataset: The original source dataset from which the MMTU instance is derived. |
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## Dataset Creation |
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Please refer to Section 3.2 in the paper. |
