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.
Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering
Institute of Computing and Intelligence, Harbin Institute of Technology (Shenzhen)（计算与智能研究院，哈尔滨工业大学（深圳））；Shenzhen Loop Area Institute (SLAI)（深圳环形区研究院（SLAI））
Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Alibaba Cloud Computing（阿里云）；Hong Kong University of Science and Technology（香港科技大学）
Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations
Department of Artificial Intelligence, Faculty of Information and Communication Technology, Wrocław University of Science and Technology（人工智能系，信息与通信技术学院，沃斯克大学）
FreeChunker: A Cross-Granularity Chunking Framework
Shanghai Institute of Artificial Intelligence for Education, East China Normal University, China（上海教育人工智能研究院，东华师范大学，中国）；Education Technology, East China Normal University, China（教育技术，东华师范大学，中国）
Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Massachusetts General Hospital, Harvard University（哈佛大学马萨诸塞州总医院）；Nota AI
QChunker: Learning Question-Aware Text Chunking for Domain RAG via Multi-Agent Debate
School of Information, Renmin University of China（中国人民大学信息学院）；School of Smart Governance, Renmin University of China（中国人民大学智能治理学院）；School of Information, BRAIN, Renmin University of China（中国人民大学信息学院、BRAIN学院）；School of Economics and Management, Tsinghua University（清华大学经济管理学院）
Towards Reliable Retrieval in RAG Systems for Large Legal Datasets
Technical University of Darmstadt（德意志联邦共和国达姆斯塔特技术大学）；University of Trento（特伦托大学）；University of Florence（佛罗伦萨大学）；University of Bologna（博洛尼亚大学）；European University Institute（欧洲大学研究所）
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering
MoE Key Lab of Artificial Intelligence（摩尔电子关键实验室）；X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University（X-LANCE实验室，计算机科学学院，上海交通大学）；Jiangsu Key Lab of Language Computing（江苏语言计算重点实验室）；AISpeech Co., Ltd.（AISpeech公司）；Suzhou Laboratory（苏州实验室）
MoM: Mixtures of Scenario-Aware Document Memories for Retrieval-Augmented Generation Systems
School of Information, Renmin University of China, Beijing, China（中国人民大学信息学院）；MemTensor (Shanghai) Technology Co., Ltd.（MemTensor（上海）技术有限公司）；Institute for Advanced Algorithms Research, Shanghai（上海先进算法研究所）
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation
Department of Computer and Data Sciences, Case Western Reserve University（计算机与数据科学系，凯斯西储大学）；Department of Design and Innovation, Case Western Reserve University（设计与创新系，凯斯西储大学）
