FLEXTAF：利用灵活的表格格式增强表格推理
FLEXTAF: Enhancing Table Reasoning with Flexible Tabular Formats
AI总结：
针对固定表格格式限制LLM表格推理性能的问题，提出FLEXTAF-Single和FLEXTAF-Vote方法，通过分类器预测最优格式或整合多格式结果，在WikiTableQuestions和TabFact上实现显著性能提升。
摘要：
表格推理任务旨在根据给定的表格回答问题。目前，使用大型语言模型（LLMs）是表格推理的主要方法。大多数现有方法采用固定的表格格式来表示表格，这可能限制性能。鉴于每个实例需要不同的能力且模型具备不同的能力，我们断言不同的实例和模型适合不同的表格格式。我们通过对实验结果的定量分析证明了上述主张，其中不同的实例和模型使用各种表格格式实现了不同的性能。基于此讨论，我们提出FLEXTAF-Single和FLEXTAF-Vote，通过采用灵活的表格格式来增强表格推理性能。具体而言，(i) FLEXTAF-Single训练一个分类器，基于实例和LLM预测最合适的表格格式。(ii) FLEXTAF-Vote整合跨不同格式的结果。我们在WikiTableQuestions和TabFact上的实验显示出显著改进，与使用固定表格格式结合贪婪解码和自一致性解码所达到的最佳性能相比，平均增益分别为2.3%和4.8%，从而验证了我们方法的有效性。
英文摘要：
The table reasoning task aims to answer the question according to the given table. Currently, using Large Language Models (LLMs) is the predominant method for table reasoning. Most existing methods employ a fixed tabular format to represent the table, which could limit the performance. Given that each instance requires different capabilities and models possess varying abilities, we assert that different instances and models suit different tabular formats. We prove the aforementioned claim through quantitative analysis of experimental results, where different instances and models achieve different performances using various tabular formats. Building on this discussion, we propose FLEXTAF-Single and FLEXTAF-Vote to enhance table reasoning performance by employing flexible tabular formats. Specifically, (i) FLEXTAF-Single trains a classifier to predict the most suitable tabular format based on the instance and the LLM. (ii) FLEXTAF-Vote integrates the results across different formats. Our experiments on WikiTableQuestions and TabFact reveal significant improvements, with average gains of 2.3% and 4.8% compared to the best performance achieved using a fixed tabular format with greedy decoding and self-consistency decoding, thereby validating the effectiveness of our methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
