CASC让大模型在复杂领域问答时更准更省，自动提炼关键信息并解决矛盾
Context-Adaptive Synthesis and Compression for Enhanced Retrieval-Augmented Generation in Complex Domains
- 用小模型分析检索到的文档，提取要点并处理冲突
- 将原始信息压缩为结构化内容，减少90%以上令牌数
- 适合科研、医疗等需精准推理的复杂领域应用
大型语言模型（LLMs）在语言任务中表现优异，但容易产生幻觉和依赖过时知识。检索增强生成（RAG）通过引入外部知识缓解此问题。然而，在涉及多篇长文档或相互矛盾信息的复杂领域，传统RAG面临信息过载和合成效率低下的挑战，导致答案不准确且不可靠。为此，我们提出CASC（上下文自适应合成与压缩）框架，智能处理检索到的上下文。CASC引入一个由微调小模型驱动的上下文分析与合成模块（CAS），实现关键信息提取、跨文档一致性检查与冲突解决，以及面向问题的结构化合成。该过程将原始零散信息转化为高度浓缩、结构化且语义丰富的上下文，显著降低最终阅读器模型的令牌数量与认知负担。我们在SciDocs-QA上评估CASC，这是一个专为复杂科学领域设计的新数据集，具有固有的冗余与冲突。大量实验表明，CASC持续优于强基线模型。
原文摘要 · Abstract (English)
Large Language Models (LLMs) excel in language tasks but are prone to hallucinations and outdated knowledge. Retrieval-Augmented Generation (RAG) mitigates these by grounding LLMs in external knowledge. However, in complex domains involving multiple, lengthy, or conflicting documents, traditional RAG suffers from information overload and inefficient synthesis, leading to inaccurate and untrustworthy answers. To address this, we propose CASC (Context-Adaptive Synthesis and Compression), a novel framework that intelligently processes retrieved contexts. CASC introduces a Context Analyzer & Synthesizer (CAS) module, powered by a fine-tuned smaller LLM, which performs key information extraction, cross-document consistency checking and conflict resolution, and question-oriented structured synthesis. This process transforms raw, scattered information into a highly condensed, structured, and semantically rich context, significantly reducing the token count and cognitive load for the final Reader LLM. We evaluate CASC on SciDocs-QA, a new challenging multi-document question answering dataset designed for complex scientific domains with inherent redundancies and conflicts. Our extensive experiments demonstrate that CASC consistently outperforms strong baselines.
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