Uncovering Limitations of Large Language Models in Information Seeking from Tables

• URL: http://arxiv.org/abs/2406.04113v1
• Date: Thu, 6 Jun 2024 14:30:59 GMT
• Title: Uncovering Limitations of Large Language Models in Information Seeking from Tables
• Authors: Chaoxu Pang, Yixuan Cao, Chunhao Yang, Ping Luo,
• Abstract summary: This paper introduces a more reliable benchmark for Table Information Seeking (TabIS) To avoid the unreliable evaluation caused by text similarity-based metrics, TabIS adopts a single-choice question format (with two options per question) instead of a text generation format.
• Score: 28.19697259795014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tables are recognized for their high information density and widespread usage, serving as essential sources of information. Seeking information from tables (TIS) is a crucial capability for Large Language Models (LLMs), serving as the foundation of knowledge-based Q&A systems. However, this field presently suffers from an absence of thorough and reliable evaluation. This paper introduces a more reliable benchmark for Table Information Seeking (TabIS). To avoid the unreliable evaluation caused by text similarity-based metrics, TabIS adopts a single-choice question format (with two options per question) instead of a text generation format. We establish an effective pipeline for generating options, ensuring their difficulty and quality. Experiments conducted on 12 LLMs reveal that while the performance of GPT-4-turbo is marginally satisfactory, both other proprietary and open-source models perform inadequately. Further analysis shows that LLMs exhibit a poor understanding of table structures, and struggle to balance between TIS performance and robustness against pseudo-relevant tables (common in retrieval-augmented systems). These findings uncover the limitations and potential challenges of LLMs in seeking information from tables. We release our data and code to facilitate further research in this field.

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