Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
- URL: http://arxiv.org/abs/2511.04700v1
- Date: Sat, 01 Nov 2025 20:08:13 GMT
- Title: Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
- Authors: Song Wang, Zihan Chen, Peng Wang, Zhepei Wei, Zhen Tan, Yu Meng, Cong Shen, Jundong Li,
- Abstract summary: WinnowRAG is designed to systematically filter out noisy documents while preserving valuable content.<n>WinnowRAG operates in two stages: In Stage I, we perform query-aware clustering to group similar documents and form distinct topic clusters.<n>In Stage II, we perform winnowing, wherein a critic LLM evaluates the outputs of multiple agents and iteratively separates useful documents from noisy ones.
- Score: 61.47019392413271
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources to address their limitations in accessing up-to-date or specialized information. A natural strategy to increase the likelihood of retrieving relevant information is to expand the number of retrieved documents. However, involving more documents could introduce significant noise, as many documents may be irrelevant or misleading, thereby reducing the overall accuracy of the generated responses. To overcome the challenge associated with handling a larger number of documents, we propose WinnowRAG, a novel RAG framework designed to systematically filter out noisy documents while preserving valuable content -- a process we refer to as winnowing. WinnowRAG operates in two stages: In Stage I, we perform query-aware clustering to group similar documents and form distinct topic clusters. Each cluster is assigned to an LLM agent for generating a unique answer. In Stage II, we perform winnowing, wherein a critic LLM evaluates the outputs of multiple agents and iteratively separates useful documents from noisy ones. To retain useful documents when discarding agents, we propose two strategic merging techniques to ensure that only relevant knowledge is used for generating the final response. Crucially, WinnowRAG is model-agnostic and does not require any model fine-tuning, making it easily adaptable to various tasks. Extensive experiments on various realistic datasets demonstrate the effectiveness of WinnowRAG over state-of-the-art baselines.
Related papers
- Towards Mixed-Modal Retrieval for Universal Retrieval-Augmented Generation [72.34977512403643]
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) by retrieving relevant documents from an external corpus.<n>Existing RAG systems primarily focus on unimodal text documents, and often fall short in real-world scenarios where both queries and documents may contain mixed modalities (such as text and images)<n>We propose Nyx, a unified mixed-modal to mixed-modal retriever tailored for Universal Retrieval-Augmented Generation scenarios.
arXiv Detail & Related papers (2025-10-20T09:56:43Z) - Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
arXiv Detail & Related papers (2025-10-17T02:33:16Z) - Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning [62.640169289390535]
SPLIT-RAG is a multi-agent RAG framework that addresses the limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval.<n>The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG.<n>The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types.<n>A hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications.
arXiv Detail & Related papers (2025-05-20T06:44:34Z) - MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation [34.66546005629471]
Large Language Models (LLMs) are essential tools for various natural language processing tasks but often suffer from generating outdated or incorrect information.<n>Retrieval-Augmented Generation (RAG) addresses this issue by incorporating external, real-time information retrieval to ground LLM responses.<n>To tackle this problem, we propose Multi-Agent Filtering Retrieval-Augmented Generation (MAIN-RAG)<n>MAIN-RAG is a training-free RAG framework that leverages multiple LLM agents to collaboratively filter and score retrieved documents.
arXiv Detail & Related papers (2024-12-31T08:07:26Z) - RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation [21.764973680014368]
RetroLLM is a unified framework that integrates retrieval and generation into a single, cohesive process.<n>To mitigate false pruning in the process of constrained evidence generation, we introduce hierarchical FM-Index constraints.<n>Experiments on five open-domain QA datasets demonstrate RetroLLM's superior performance across both in-domain and out-of-domain tasks.
arXiv Detail & Related papers (2024-12-16T16:03:25Z) - DMQR-RAG: Diverse Multi-Query Rewriting for RAG [26.518517678671376]
Large language models often encounter challenges with static knowledge and hallucinations, which undermine their reliability.
We introduce DMQR-RAG, a Diverse Multi-Query Rewriting framework to improve the performance of both document retrieval and final responses in RAG.
arXiv Detail & Related papers (2024-11-20T09:43:30Z) - VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents [66.42579289213941]
Retrieval-augmented generation (RAG) is an effective technique that enables large language models to utilize external knowledge sources for generation.<n>We introduce VisRAG, which tackles this issue by establishing a vision-language model (VLM)-based RAG pipeline.<n>In this pipeline, instead of first parsing the document to obtain text, the document is directly embedded using a VLM as an image and then retrieved to enhance the generation of a VLM.
arXiv Detail & Related papers (2024-10-14T15:04:18Z) - DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering [4.364937306005719]
RAG has recently demonstrated the performance of Large Language Models (LLMs) in the knowledge-intensive tasks such as Question-Answering (QA)
We have found that even though there is low relevance between some critical documents and query, it is possible to retrieve the remaining documents by combining parts of the documents with the query.
A two-stage retrieval framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is proposed to improve document retrieval recall and the accuracy of answers.
arXiv Detail & Related papers (2024-06-11T15:15:33Z) - Multi-Head RAG: Solving Multi-Aspect Problems with LLMs [18.48202014877111]
Multi-Head RAG (MRAG) is a novel scheme for fetching multi-aspect documents.<n>We show MRAG's design advantages over 18 RAG baselines, empirical improvements of up to 20% in retrieval success ratios.
arXiv Detail & Related papers (2024-06-07T16:59:38Z) - Continual Learning for Generative Retrieval over Dynamic Corpora [115.79012933205756]
Generative retrieval (GR) directly predicts the identifiers of relevant documents (i.e., docids) based on a parametric model.<n>The ability to incrementally index new documents while preserving the ability to answer queries is vital to applying GR models.<n>We put forward a novel Continual-LEarner for generatiVE Retrieval (CLEVER) model and make two major contributions to continual learning for GR.
arXiv Detail & Related papers (2023-08-29T01:46:06Z) - Generator-Retriever-Generator Approach for Open-Domain Question Answering [18.950517545413813]
We propose a novel approach that combines document retrieval techniques with a large language model (LLM)
In parallel, a dual-encoder network retrieves documents that are relevant to the question from an external corpus.
GRG outperforms the state-of-the-art generate-then-read and retrieve-then-read pipelines.
arXiv Detail & Related papers (2023-07-21T00:34:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.