用短文本训练的压缩器，让长文档问答更快更准
BRIEF-Pro: Universal Context Compression with Short-to-Long Synthesis for Fast and Accurate Multi-Hop Reasoning
- 用短上下文训练，压缩超万字长文为精炼摘要
- 压缩率32倍时，问答准确率比同行高4.67%且算力开销仅23%
- 支持自定义摘要长度，适配各类大模型快速推理
随着检索增强生成（RAG）应对复杂任务，上下文信息虽日益丰富，却带来更高延迟与模型认知负担。为缓解这一瓶颈，尤其针对多跳问题，我们提出BRIEF-Pro。它是一种通用轻量级压缩器，可从检索文档中提炼出与查询相关的证据，并生成简洁摘要无缝集成到上下文中的RAG流程。利用少于1000词的种子数据，BRIEF-Pro被训练用于对超过10,000词的扩展上下文进行抽象压缩，适用于多种场景。此外，用户可通过指定句子数灵活控制摘要长度。在四个开放域多跳问答数据集上的实验表明，BRIEF-Pro生成的摘要更短且相关性更强，显著提升小、大及专有语言模型的表现。使用70B规模阅读器模型时，其32倍压缩率相比LongLLMLingua的9倍压缩率，在平均性能上提高4.67%，同时仅需后者23%的计算开销。
原文摘要 · Abstract (English)
As retrieval-augmented generation (RAG) tackles complex tasks, increasingly expanded contexts offer richer information, but at the cost of higher latency and increased cognitive load on the model. To mitigate this bottleneck, especially for intricate multi-hop questions, we introduce BRIEF-Pro. It is a universal, lightweight compressor that distills relevant evidence for a given query from retrieved documents into a concise summary for seamless integration into in-context RAG. Using seed data consisting of relatively short contexts (fewer than 1k words), BRIEF-Pro is trained to perform abstractive compression of extended contexts exceeding 10k words across a wide range of scenarios. Furthermore, BRIEF-Pro offers flexible user control over summary length by allowing users to specify the desired number of sentences. Experiments on four open-domain multi-hop question-answering datasets show that BRIEF-Pro generates more concise and relevant summaries, enhancing performance across small, large, and proprietary language models. With the 70B reader model, 32x compression by BRIEF-Pro improves QA performance by 4.67% on average over LongLLMLingua's 9x, while requiring only 23% of its computational overhead.
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