Context-Adaptive Synthesis and Compression for Enhanced Retrieval-Augmented Generation in Complex Domains

--Large Language Models (LLMs) excel in language tasks but are prone to hallucinations and outdated knowledge.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. T o 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. Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding, generating, and processing human language across a wide array of tasks [1]. However, despite their impressive fluency and reasoning abilities, LLMs inherently suffer from several critical limitations, including the propensity for "hallucinations" (generating factually incorrect or nonsensical information) and a knowledge cut-off date, rendering them incapable of accessing the most current information [2].