Table Question Answering
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Table Question Answering
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Table Question Answering
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ST Raptor LLM Powered Semi Structured Table Question Answering AI
 have markedly enhanced the interpretation and processing of tabular data%2C introducing previously unimaginable capabilities. Despite these achievements%2C LLMs still encounter significant challenges when applied in industrial scenarios%2C particularly due to the increased complexity of reasoning required with real-world tabular data%2C underscoring a notable disparity between academic benchmarks and practical applications. To address this discrepancy%2C we conduct a detailed investigation into the application of tabular data in industrial scenarios and propose a comprehensive and complex benchmark TableBench%2C including 18 fields within four major categories of table question answering (TableQA) capabilities. Furthermore%2C we introduce TableLLM%2C trained on our meticulously constructed training set TableInstruct%2C achieving comparable performance with GPT-3.5. Massive experiments conducted on TableBench indicate that both open-source and proprietary LLMs still have significant room for improvement to meet real-world demands%2C where the most advanced model%2C GPT-4%2C achieves only a modest score compared to humans. "TableBench A Comprehensive And Complex Benchmark For Table Question")
TableBench A Comprehensive And Complex Benchmark For Table Question
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