NeedleInATable: Exploring Long-Context Capability of Large Language Models towards Long-Structured Tables

• URL: http://arxiv.org/abs/2504.06560v1
• Date: Wed, 09 Apr 2025 03:46:56 GMT
• Title: NeedleInATable: Exploring Long-Context Capability of Large Language Models towards Long-Structured Tables
• Authors: Lanrui Wang, Mingyu Zheng, Hongyin Tang, Zheng Lin, Yanan Cao, Jingang Wang, Xunliang Cai, Weiping Wang,
• Abstract summary: We introduce NeedleInATable (NIAT), a novel task that treats each table cell as a "needle" and requires the model to extract the target cell under different queries.<n>We propose a data synthesis method to enhance models' long-table comprehension capabilities.
• Score: 32.9031799179503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Processing structured tabular data, particularly lengthy tables, constitutes a fundamental yet challenging task for large language models (LLMs). However, existing long-context benchmarks primarily focus on unstructured text, neglecting the challenges of long and complex structured tables. To address this gap, we introduce NeedleInATable (NIAT), a novel task that treats each table cell as a "needle" and requires the model to extract the target cell under different queries. Evaluation results of mainstream LLMs on this benchmark show they lack robust long-table comprehension, often relying on superficial correlations or shortcuts for complex table understanding tasks, revealing significant limitations in processing intricate tabular data. To this end, we propose a data synthesis method to enhance models' long-table comprehension capabilities. Experimental results show that our synthesized training data significantly enhances LLMs' performance on the NIAT task, outperforming both long-context LLMs and long-table agent methods. This work advances the evaluation of LLMs' genuine long-structured table comprehension capabilities and paves the way for progress in long-context and table understanding applications.

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