让大模型更高效处理表格，自动删减无用行列
What to Keep and What to Drop: Adaptive Table Filtering Framework
- 根据问题智能筛选表格，保留关键行和列
- 减少70%表格数据，提升跨领域问答表现
- 无需重训练，适合各类表格推理任务
面向表格推理的大语言模型常因输入长度限制难以处理大型表格。本文提出ATF（自适应表格过滤框架），一种模块化且基于问题感知的过滤流程，通过LLM生成的列描述、聚类分析及稀疏-密集对齐分数，自动剔除不相关信息的行与列。ATF可无缝集成至现有模型（如TAPAS、TAPEX）而无需重新训练。实验表明，ATF使表格单元格数量减少70%，在跨域表格问答任务中显著提升性能；而在表格事实验证任务中因丢失完整上下文导致轻微性能下降。结果表明，ATF能根据不同任务需求动态平衡信息量与简洁性。代码已开源：https://github.com/torijune/ATF-Adaptive-Table-Filtering-Framework。
原文摘要 · Abstract (English)
Large language models (LLMs) for table-based reasoning often struggle with large tables due to input length limits. We propose ATF (Adaptive Table Filtering Framework), a modular and question-aware filtering pipeline that prunes uninformative columns and rows using LLM-generated column descriptions, clustering, and sparse-dense alignment scores. ATF integrates seamlessly with existing models (e.g., TAPAS, TAPEX) without retraining. Experiments show that ATF reduces table cells by 70%, boosting performance on out-of-domain TableQA tasks while causing slight performance drops on Table Fact Verification, where full-table context is more critical. These results highlight ATF's ability to adaptively balance informativeness and minimalism across tasks. Our code available at: https://github.com/torijune/ATF-Adaptive-Table-Filtering-Framework
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