Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach

Understand

Multi-hop question answering is a challenging task with distinct industrial relevance, and Retrieval-Augmented Generation (RAG) methods based on large language models (LLMs) have become a popular approach to tackle this task.

• Owing to the potential inability to retrieve all necessary information in a single iteration, a series of iterative RAG methods has been recently developed, showing significant performance improvements.
• However, existing methods still face two critical challenges: context overload resulting from multiple rounds of retrieval, and over-planning and repetitive planning due to the lack of a recorded retrieval trajectory.
• In this paper, we propose a novel iterative RAG method called ReSP, equipped with a dual-function summarizer.