With the Generative Pre-trained Transformer 3.5 (GPT-3.5) exhibiting
remarkable reasoning and comprehension abilities in Natural Language Processing
(NLP), most Question Answering (QA) research has primarily centered around
general QA tasks based on GPT, neglecting the specific challenges posed by
Complex Table QA. In this paper, we propose to incorporate GPT-3.5 to address
such challenges, in which complex tables are reconstructed into tuples and
specific prompt designs are employed for dialogues. Specifically, we encode
each cell's hierarchical structure, position information, and content as a
tuple. By enhancing the prompt template with an explanatory description of the
meaning of each tuple and the logical reasoning process of the task, we
effectively improve the hierarchical structure awareness capability of GPT-3.5
to better parse the complex tables. Extensive experiments and results on
Complex Table QA datasets, i.e., the open-domain dataset HiTAB and the aviation
domain dataset AIT-QA show that our approach significantly outperforms previous
work on both datasets, leading to state-of-the-art (SOTA) performance.

本文中，我们提出了在复杂表格问答中引入 GPT-3.5 的方法来解决相关挑战，通过重构复杂表格为元组并使用特定的提示设计对话框，将每个单元格的层次结构、位置信息和内容编码为一个元组，并通过加强提示模板的解释说明每个元组的含义和任务的逻辑推理过程，有效提高了 GPT-3.5 对复杂表格的层次结构感知能力，从而更好地解析复杂表格。通过大量的实验证明，我们的方法在复杂表格问答数据集 HiTAB 和航空领域数据集 AIT-QA 上显著优于先前的工作，取得了最先进的性能。