发表机构
The University of Melbourne（墨尔本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对LLM处理长表格问答时准确率下降的问题，提出TabScope框架，通过问题自适应选择局部或全表推理，在WikiTQ和SLQA基准上取得最佳整体性能。
AI 中文摘要
大型语言模型（LLM）在表格问答任务中表现出较强性能，但随着表格规模增大，其准确率往往会下降。我们发现这种下降在不同问题类型中并不均匀：对位置敏感的问题尤其会受到表格无关内容的影响，而需要更广泛证据的问题仍可从全表推理中受益。基于该观察，我们提出一种问题自适应框架，可在局部推理与全表推理间动态选择。该框架通过操作感知的表格分解构建问题特定的子表，并利用预测的问题类型确定合适的推理模式。我们进一步引入用于评估证据选择的银标参考子表，并构建基于真实世界长表格的基准SLQA。在WikiTQ和SLQA上的实验表明，局部化对查找和局部推理问题尤其有效，而在局部与全表推理间的自适应选择可取得最佳整体性能。这些结果表明，长表格问答不仅需要决定如何局部化，还需决定何时局部化。我们的代码和数据集将在论文发表后公开。
英文摘要
Large Language Models (LLMs) have shown strong performance on table question answering, yet their accuracy often degrades as table size increases. We find that this degradation is not uniform across question types. Localization-sensitive questions are particularly affected by irrelevant table content, while questions requiring broader evidence may still benefit from full-table reasoning. Based on this observation, we propose a question-adaptive framework that dynamically selects between localized and full-table reasoning. The framework constructs question-specific sub-tables through operation-aware table decomposition and uses the predicted question type to determine the appropriate reasoning mode. We further introduce silver reference sub-tables for evaluating evidence selection and construct SLQA, a benchmark based on real-world long tables. Experiments on WikiTQ and SLQA show that localization is particularly effective for lookup and local reasoning questions, while adaptive selection between localized and full-table reasoning achieves the best overall performance. These results highlight that long-table QA requires deciding not only how to localize, but also when to localize. Our code and datasets will be made available upon publication of the paper.
Commentsconference paper preprint
