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@@ -21,7 +21,7 @@ Large Language Models (LLMs) are showing emerging abilities, and one of the late
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  reasoning in question answering on tabular data. Although there are some available datasets to assess question
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  answering systems on tabular data, they are not large and diverse enough to evaluate this new ability of LLMs.
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  To this end, we provide a corpus of 65 real world datasets, with 3,269,975 and 1615 columns in total, and 1300 questions to evaluate your models for the task of QA over Tabular Data.
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- By clicking on each in the table below, you will be able to explore each dataset.
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  | | Name | Rows | Cols | Domain | Source (Reference) |
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  |---:|:-------------------------------|-------:|-------:|:---------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
 
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  reasoning in question answering on tabular data. Although there are some available datasets to assess question
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  answering systems on tabular data, they are not large and diverse enough to evaluate this new ability of LLMs.
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  To this end, we provide a corpus of 65 real world datasets, with 3,269,975 and 1615 columns in total, and 1300 questions to evaluate your models for the task of QA over Tabular Data.
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+ By clicking on each name in the table below, you will be able to explore each dataset.
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  | | Name | Rows | Cols | Domain | Source (Reference) |
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  |---:|:-------------------------------|-------:|-------:|:---------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|