Uncovering Limitations of Large Language Models in Information Seeking from Tables
This work addresses the lack of reliable evaluation for LLMs in table information seeking, which is crucial for knowledge-based Q&A systems, but it is incremental as it focuses on benchmarking rather than proposing new methods.
The paper tackled the problem of evaluating Large Language Models (LLMs) in seeking information from tables by introducing a new benchmark called TabIS, which uses single-choice questions to avoid unreliable text similarity metrics, and found that most LLMs perform inadequately, with GPT-4-turbo only marginally satisfactory.
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.