Beyond decomposition: Hierarchical dependency management in multi‐document question answering

Abstract When using retrieval‐augmented generation (RAG) to handle multi‐document question answering (MDQA) tasks, it is beneficial to decompose complex queries into multiple simpler ones to enhance retrieval results. However, previous strategies always employ a one‐shot approach of question decomposition, overlooking subquestions dependency problem and failing to ensure that the derived subqueries are single‐hop. To overcome this challenge, we introduce a novel framework called DSRC‐QCS. Decompose‐solve‐renewal‐cycle (DSRC) is an iterative multi‐hop question processing module. The key idea of DSRC involves using a unique symbol to achieve hierarchical dependency management and employing a cyclical process of question decomposition, solving, and renewal to continuously generate and resolve all single‐hop subquestions. Query‐chain selector (QCS) functions as a voting mechanism that effectively utilizes the reasoning process of DSRC to assess and select solutions. We compare DSRC‐QCS against five RAG approaches across three datasets and three LLMs. DSRC‐QCS demonstrates superior performance. Compared to the Direct Retrieval method, DSRC‐QCS improves the average F1 score by 17.36% with Alpaca‐7b, 10.83% with LLaMa2‐Chat‐7b, and 11.88% with GPT‐3.5‐Turbo. We also conduct ablation studies to validate the performance of both DSRC and QCS and explore factors influencing the effectiveness of DSRC. We have included all prompts in the Appendix.