HRoT: Hybrid prompt strategy and Retrieval of Thought for Table-Text Hybrid Question Answering

• URL: http://arxiv.org/abs/2309.12669v1
• Date: Fri, 22 Sep 2023 07:26:17 GMT
• Title: HRoT: Hybrid prompt strategy and Retrieval of Thought for Table-Text Hybrid Question Answering
• Authors: Tongxu Luo, Fangyu Lei, Jiahe Lei, Weihao Liu, Shihu He, Jun Zhao and Kang Liu
• Abstract summary: In this paper, we introduce a new prompting strategy called Hybrid prompt strategy and Retrieval of Thought for TextTableQA. Our method achieves superior performance compared to the fully-supervised SOTA on the MultiHiertt dataset in the few-shot setting.
• Score: 13.026990720973703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Answering numerical questions over hybrid contents from the given tables and text(TextTableQA) is a challenging task. Recently, Large Language Models (LLMs) have gained significant attention in the NLP community. With the emergence of large language models, In-Context Learning and Chain-of-Thought prompting have become two particularly popular research topics in this field. In this paper, we introduce a new prompting strategy called Hybrid prompt strategy and Retrieval of Thought for TextTableQA. Through In-Context Learning, we prompt the model to develop the ability of retrieval thinking when dealing with hybrid data. Our method achieves superior performance compared to the fully-supervised SOTA on the MultiHiertt dataset in the few-shot setting.

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