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  # TableEval dataset
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  **TableEval** is developed to benchmark and compare the performance of (M)LLMs on tables from scientific vs. non-scientific sources, represented as images vs. text.
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- It comprises six data subsets derived from existing benchmarks for question answering (QA) and table-to-text (T2T) tasks, containing a total of **3017 tables** and **11312 instances**.
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  The scienfific subset includes tables from pre-prints and peer-reviewed scholarly publications, while the non-scientific subset involves tables from Wikipedia and financial reports.
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  Each table is available as a **PNG** image and in four textual formats: **HTML**, **XML**, **LaTeX**, and **Dictionary (Dict)**.
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  All task annotations are taken from the source datasets.
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  │ │ ├── numericnlg.json
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  └── └── └── numericnlg_imgs.zip
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- For more details on each subset, please, refer to the respective README.md files: [ComTQA](ComTQA/README.md), Logic2Text, LogicNLG, SciGen, numericNLG.
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  ## Citation
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  # TableEval dataset
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  **TableEval** is developed to benchmark and compare the performance of (M)LLMs on tables from scientific vs. non-scientific sources, represented as images vs. text.
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+ It comprises six data subsets derived from the test sets of existing benchmarks for question answering (QA) and table-to-text (T2T) tasks, containing a total of **3017 tables** and **11312 instances**.
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  The scienfific subset includes tables from pre-prints and peer-reviewed scholarly publications, while the non-scientific subset involves tables from Wikipedia and financial reports.
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  Each table is available as a **PNG** image and in four textual formats: **HTML**, **XML**, **LaTeX**, and **Dictionary (Dict)**.
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  All task annotations are taken from the source datasets.
 
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  │ │ ├── numericnlg.json
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  └── └── └── numericnlg_imgs.zip
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+ For more details on each subset, please, refer to the respective README.md files: [ComTQA](ComTQA/README.md), [Logic2Text](Logic2Text/README.md), LogicNLG, SciGen, numericNLG.
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  ## Citation
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