BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression
Yuankai Li, Jia-Chen Gu, Di Wu, Kai-Wei Chang, Nanyun Peng · 2025
Retrieval-augmented generation (RAG) can supplement large language models (LLMs) by integrating external knowledge.However, as the number of retrieved documents increases, the input length to LLMs grows linearly, causing a dramatic increase in latency and a degradation in long-context understanding.This is particularly serious for multi-hop questions that require a chain of reasoning across documents.To accelerate inference, reduce costs, and minimize distractions, this paper presents BRIEF (Bridging Retrieval and Inference through Evidence Fusion), a lightweight approach that performs query-aware multi-hop reasoning by compressing retrieved documents into highly dense textual summaries to integrate into incontext RAG.To enable learning compression for multi-hop reasoning, we curate synthetic data by extracting atomic propositions that encapsulate distinct factoids from the source documents to compose synthetic summaries.Based on our synthetic data built entirely by open-source models, BRIEF generates more concise summaries and enables a range of LLMs to achieve exceptional open-domain question answering (QA) performance.For example, on HotpotQA, BRIEF improves the compression rate by 2 times compared to the state-of-the-art baseline, while outperforming it by 3.00% EM and 4.16% F1 with Flan-UL2 as the reader model.It also generates more concise summaries than proprietary GPT-3.5, while demonstrating nearly identical QA performance 1 .