用混合检索方法自动提取复杂表格信息，准确率达74.6%。
A Hybrid Search for Complex Table Question Answering in Securities Report
- 结合语言模型与TF-IDF的混合检索定位相关行列
- 通过对比学习训练模型，在真实数据集上达74.6%准确率
- 适合处理证券报告等含复杂表头的文档问答任务
近年来，大语言模型（LLM）在表格问答（TQA）领域备受关注，尤其用于从文档表格中提取信息。然而，直接将完整表格作为长文本输入大多数LLM，常因无法理解复杂表格结构导致错误答案。本文提出一种无需人工标注的单元格提取方法，适用于复杂表头场景。该方法通过混合检索机制，计算问题与各单元格的相似度以估计表头，再选取最相关行与列交点处的单元格作为答案。语言模型基于少量问题-表头对样本，采用对比学习进行训练以提升性能。我们在NTCIR-18 U4共享任务的TQA数据集上评估，结果表明该方法准确率达74.6%，优于GPT-4o mini（63.9%）。未来计划引入更高效的文本搜索模型，进一步缩小与人类评估结果的差距。
原文摘要 · Abstract (English)
Recently, Large Language Models (LLMs) are gaining increased attention in the domain of Table Question Answering (TQA), particularly for extracting information from tables in documents. However, directly entering entire tables as long text into LLMs often leads to incorrect answers because most LLMs cannot inherently capture complex table structures. In this paper, we propose a cell extraction method for TQA without manual identification, even for complex table headers. Our approach estimates table headers by computing similarities between a given question and individual cells via a hybrid retrieval mechanism that integrates a language model and TF-IDF. We then select as the answer the cells at the intersection of the most relevant row and column. Furthermore, the language model is trained using contrastive learning on a small dataset of question-header pairs to enhance performance. We evaluated our approach in the TQA dataset from the U4 shared task at NTCIR-18. The experimental results show that our pipeline achieves an accuracy of 74.6\%, outperforming existing LLMs such as GPT-4o mini~(63.9\%). In the future, although we used traditional encoder models for retrieval in this study, we plan to incorporate more efficient text-search models to improve performance and narrow the gap with human evaluation results.
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