一作ACL 2026Findings· Findings of ACL
BRIEF -Pro: Universal Context Compression with Short-to-Long Synthesis for Fast and Accurate Multi-Hop Reasoning
Jia-Chen Gu · Junyi Zhang · Di Wu · Yuankai Li · Kai-Wei Chang · Nanyun Peng
摘要
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. I...