Question answering (QA) over tables and text has gained much popularity over the years. Multi-hop table-text QA requires multiple hops between the table and text, making it a challenging QA task. Although several works have attempted to solve the table-text QA task, most involve training the models and requiring labeled data. In this paper, we have proposed a Retrieval Augmented Generation (RAG) based model - TTQA-RS: A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization. Our model uses an enhanced retriever for table-text information retrieval and uses augmented knowledge, including table-text summary with decomposed sub-questions with answers for a reasoning-based table-text QA. Using open-source language models, our model outperformed all existing prompting methods for table-text QA tasks on existing table-text QA datasets, such as HybridQA and OTT-QA's development set. Our experiments demonstrate the potential of prompt-based approaches using open-source LLMs. Additionally, by using LLaMA3-70B, our model achieved state-of-the-art performance for prompting-based methods on multi-hop table-text QA.

翻译：多年来，基于表格和文本的问答（QA）研究日益受到关注。多跳表格-文本问答需要在表格与文本之间进行多次信息跳转，这使其成为一项极具挑战性的QA任务。尽管已有若干研究尝试解决表格-文本QA问题，但大多涉及模型训练且需要标注数据。本文提出了一种基于检索增强生成（RAG）的模型——TTQA-RS：一种融合推理与摘要的多跳表格-文本问答分解提示方法。该模型采用增强型检索器进行表格-文本信息检索，并利用增强知识（包括表格-文本摘要及分解后的子问题答案）实现基于推理的表格-文本QA。通过使用开源语言模型，本模型在现有表格-文本QA数据集（如HybridQA和OTT-QA开发集）上的表现超越了所有现有的表格-文本QA提示方法。实验结果表明，基于提示的方法结合开源大语言模型具有巨大潜力。此外，通过使用LLaMA3-70B模型，本方法在多跳表格-文本QA的提示式方法中达到了最先进的性能水平。