Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

• URL: http://arxiv.org/abs/2305.13062v5
• Date: Wed, 17 Jul 2024 05:26:03 GMT
• Title: Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study
• Authors: Yuan Sui, Mengyu Zhou, Mingjie Zhou, Shi Han, Dongmei Zhang,
• Abstract summary: Large language models (LLMs) are becoming attractive as few-shot reasoners to solve Natural Language (NL)-related tasks. We try to understand this by designing a benchmark to evaluate the structural understanding capabilities of LLMs. We propose $textitself-augmentation$ for effective structural prompting, such as critical value / range identification.
• Score: 44.39031420687302
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) are becoming attractive as few-shot reasoners to solve Natural Language (NL)-related tasks. However, the understanding of their capability to process structured data like tables remains an under-explored area. While tables can be serialized as input for LLMs, there is a lack of comprehensive studies on whether LLMs genuinely comprehend this data. In this paper, we try to understand this by designing a benchmark to evaluate the structural understanding capabilities of LLMs through seven distinct tasks, e.g., cell lookup, row retrieval and size detection. Specially, we perform a series of evaluations on the recent most advanced LLM models, GPT-3.5 and GPT-4 and observe that performance varied with different input choices, including table input format, content order, role prompting, and partition marks. Drawing from the insights gained through the benchmark evaluations, we propose $\textit{self-augmentation}$ for effective structural prompting, such as critical value / range identification using internal knowledge of LLMs. When combined with carefully chosen input choices, these structural prompting methods lead to promising improvements in LLM performance on a variety of tabular tasks, e.g., TabFact($\uparrow2.31\%$), HybridQA($\uparrow2.13\%$), SQA($\uparrow2.72\%$), Feverous($\uparrow0.84\%$), and ToTTo($\uparrow5.68\%$). We believe that our open source benchmark and proposed prompting methods can serve as a simple yet generic selection for future research. The code and data of this paper will be temporality released at https://anonymous.4open.science/r/StructuredLLM-76F3/README.md and will be replaced with an official one at https://github.com/microsoft/TableProvider later.

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