A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models

• URL: http://arxiv.org/abs/2311.07491v1
• Date: Mon, 13 Nov 2023 17:28:03 GMT
• Title: A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models
• Authors: Hejing Cao and Zhenwei An and Jiazhan Feng and Kun Xu and Liwei Chen and Dongyan Zhao
• Abstract summary: We introduce the "Decompose-and-Query" framework (D&Q) This framework guides the model to think and utilize external knowledge similar to ReAct. On our ChitChatQA dataset, D&Q does not lose to ChatGPT in 67% of cases.
• Score: 43.10340493000934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large language models exhibit remarkable performance in the Question Answering task, they are susceptible to hallucinations. Challenges arise when these models grapple with understanding multi-hop relations in complex questions or lack the necessary knowledge for a comprehensive response. To address this issue, we introduce the "Decompose-and-Query" framework (D&Q). This framework guides the model to think and utilize external knowledge similar to ReAct, while also restricting its thinking to reliable information, effectively mitigating the risk of hallucinations. Experiments confirm the effectiveness of D&Q: On our ChitChatQA dataset, D&Q does not lose to ChatGPT in 67% of cases; on the HotPotQA question-only setting, D&Q achieved an F1 score of 59.6%. Our code is available at https://github.com/alkaidpku/DQ-ToolQA.

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