提出TaDRe模型，提升大半结构化表格问答准确率
Towards Question Answering over Large Semi-structured Tables
- 通过预处理与后处理双重优化表分解质量
- 在新构建的大表基准上达到当前最优性能
- 适合需要高精度表格问答的科研与工程场景
表格问答（TableQA）因网页中广泛存在的半结构化表格数据而备受关注。尽管已有诸多研究，大表格上的表格问答仍是开放挑战，因为大型表格可能超出模型全量理解能力。现有方法通过生成程序解析表格并分解为较小的相关子表来降低输入规模，但此类方法易受程序生成与执行错误影响，难以保证分解质量。为此，我们提出TaDRe模型，结合预处理与后处理的双重表分解优化机制，确保分解质量，从而实现高精度的表格问答。为评估该模型，我们利用大语言模型驱动的表格扩展与问答对生成，构建了两个新的大规模表格问答基准。在新基准及公开基准上的大量实验表明，TaDRe在大表表格问答任务中达到当前最优表现。
原文摘要 · Abstract (English)
Table Question Answering (TableQA) attracts strong interests due to the prevalence of web information presented in the form of semi-structured tables. Despite many efforts, TableQA over large tables remains an open challenge. This is because large tables may overwhelm models that try to comprehend them in full to locate question answers. Recent studies reduce input table size by decomposing tables into smaller, question-relevant sub-tables via generating programs to parse the tables. However, such solutions are subject to program generation and execution errors and are difficult to ensure decomposition quality. To address this issue, we propose TaDRe, a TableQA model that incorporates both pre- and post-table decomposition refinements to ensure table decomposition quality, hence achieving highly accurate TableQA results. To evaluate TaDRe, we construct two new large-table TableQA benchmarks via LLM-driven table expansion and QA pair generation. Extensive experiments on both the new and public benchmarks show that TaDRe achieves state-of-the-art performance on large-table TableQA tasks.
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