A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question
  Decomposition with Large Language Models
Hejing Cao Zhenwei An Jiazhan Feng Kun Xu Liwei Chen Dongyan Zhao
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.