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@@ -21,6 +21,9 @@ 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 name in the table below, you will be able to explore each dataset.
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  | | Name | Rows | Cols | Domain | Source (Reference) |
@@ -118,7 +121,7 @@ In the table below you can see all the data types assigned to each column, as we
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  | list[category] | 112 | [apple, orange, banana] |
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  | list[url] | 8 | [google.com, apple.com] |
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- ### Reference
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  If you use this resource, please use the following reference
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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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+ ## 📚 Datasets
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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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  | list[category] | 112 | [apple, orange, banana] |
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  | list[url] | 8 | [google.com, apple.com] |
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+ ## 🔗 Reference
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  If you use this resource, please use the following reference
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