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 model - TTQA-RS: A break-down
prompting approach for Multi-hop Table-Text Question Answering with Reasoning
and Summarization. Our model uses augmented knowledge including table-text
summary with decomposed sub-question with answer 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 like HybridQA and OTT-QA's development set. Our results are comparable
with the training-based state-of-the-art models, demonstrating the potential of
prompt-based approaches using open-source LLMs. Additionally, by using GPT-4
with LLaMA3-70B, our model achieved state-of-the-art performance for
prompting-based methods on multi-hop table-text QA.

使用开源语言模型，我们提出了一种基于推理和摘要的分解问题和回答的多跳表 - 文本问答的 TTQA-RS 模型，它在现有的 HybridQA 和 OTT-QA 数据集上的表格 - 文本 QA 任务中表现出色，同时与基于训练的最先进模型相当，展示了基于提示的方法利用开源 LLMs 的潜力，此外，通过使用 GPT-4 与 LLaMA3-70B，我们的模型在多跳表 - 文本 QA 的基于提示的方法上实现了最先进的性能。