This paper presents a novel Chunking-Free In-Context (CFIC) retrieval approach, specifically tailored for Retrieval-Augmented Generation (RAG) systems. Traditional RAG systems often struggle with grounding responses using precise evidence text due to the challenges of processing lengthy documents and filtering out irrelevant content. Commonly employed solutions, such as document chunking and adapting language models to handle longer contexts, have their limitations. These methods either disrupt the semantic coherence of the text or fail to effectively address the issues of noise and inaccuracy in evidence retrieval. CFIC addresses these challenges by circumventing the conventional chunking process. It utilizes the encoded hidden states of documents for in-context retrieval, employing auto-aggressive decoding to accurately identify the specific evidence text required for user queries, eliminating the need for chunking. CFIC is further enhanced by incorporating two decoding strategies, namely Constrained Sentence Prefix Decoding and Skip Decoding. These strategies not only improve the efficiency of the retrieval process but also ensure that the fidelity of the generated grounding text evidence is maintained. Our evaluations of CFIC on a range of open QA datasets demonstrate its superiority in retrieving relevant and accurate evidence, offering a significant improvement over traditional methods. By doing away with the need for document chunking, CFIC presents a more streamlined, effective, and efficient retrieval solution, making it a valuable advancement in the field of RAG systems.

翻译：本文提出了一种新颖的无分块上下文（CFIC）检索方法，专门针对检索增强生成（RAG）系统设计。传统RAG系统常因处理长文档及过滤无关内容的挑战，难以利用精确证据文本来支撑回答。现有解决方案（如文档分块、扩展语言模型上下文窗口）存在局限性：前者破坏文本语义连贯性，后者无法有效解决证据检索中的噪声与不准确性问题。CFIC通过绕过传统分块流程应对这些挑战，利用文档的编码隐藏状态进行上下文检索，并采用自回归解码在无需分块的情况下精确识别用户查询所需的证据文本。为进一步提升性能，CFIC引入了两种解码策略——约束句子前缀解码与跳跃解码，这些策略不仅提高了检索效率，还确保了生成接地文本证据的保真度。我们在多个开放域问答数据集上的评估表明，CFIC在检索相关且准确的证据方面优于传统方法。通过消除对文档分块的需求，CFIC提供了一种更简洁、高效且有效的检索方案，成为RAG系统领域的重要进展。