ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems

• URL: http://arxiv.org/abs/2410.19572v4
• Date: Tue, 19 Nov 2024 10:00:41 GMT
• Title: ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems
• Authors: Ishneet Sukhvinder Singh, Ritvik Aggarwal, Ibrahim Allahverdiyev, Muhammad Taha, Aslihan Akalin, Kevin Zhu, Sean O'Brien,
• Abstract summary: Retrieval-Augmented Generation (RAG) systems generate inaccurate responses due to the retrieval of irrelevant or loosely related information. We propose ChunkRAG, a framework that enhances RAG systems by evaluating and filtering retrieved information at the chunk level.
• Score: 2.8692611791027893
• License:
• Abstract: Retrieval-Augmented Generation (RAG) systems using large language models (LLMs) often generate inaccurate responses due to the retrieval of irrelevant or loosely related information. Existing methods, which operate at the document level, fail to effectively filter out such content. We propose LLM-driven chunk filtering, ChunkRAG, a framework that enhances RAG systems by evaluating and filtering retrieved information at the chunk level. Our approach employs semantic chunking to divide documents into coherent sections and utilizes LLM-based relevance scoring to assess each chunk's alignment with the user's query. By filtering out less pertinent chunks before the generation phase, we significantly reduce hallucinations and improve factual accuracy. Experiments show that our method outperforms existing RAG models, achieving higher accuracy on tasks requiring precise information retrieval. This advancement enhances the reliability of RAG systems, making them particularly beneficial for applications like fact-checking and multi-hop reasoning.

Related papers

• Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation [43.630437906898635]
  We propose a novel two-stage fine-tuning architecture called Invar-RAG. In the retrieval stage, an LLM-based retriever is constructed by integrating LoRA-based representation learning. In the generation stage, a refined fine-tuning method is employed to improve LLM accuracy in generating answers based on retrieved information.
  arXiv  Detail & Related papers  (2024-11-11T14:25:37Z)
• RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards [78.74923079748521]
  Retrieval-Augmented Generation (RAG) has proven its effectiveness in mitigating hallucinations in Large Language Models (LLMs) Current approaches use instruction tuning to optimize LLMs, improving their ability to utilize retrieved knowledge. We propose a Differentiable Data Rewards ( DDR) method, which trains RAG systems by aligning data preferences between different RAG modules.
  arXiv  Detail & Related papers  (2024-10-17T12:53:29Z)
• Optimizing Query Generation for Enhanced Document Retrieval in RAG [53.10369742545479]
  Large Language Models (LLMs) excel in various language tasks but they often generate incorrect information. Retrieval-Augmented Generation (RAG) aims to mitigate this by using document retrieval for accurate responses.
  arXiv  Detail & Related papers  (2024-07-17T05:50:32Z)
• Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting [68.90949377014742]
  Speculative RAG is a framework that leverages a larger generalist LM to efficiently verify multiple RAG drafts produced in parallel by a smaller, distilled specialist LM. Our method accelerates RAG by delegating drafting to the smaller specialist LM, with the larger generalist LM performing a single verification pass over the drafts. It notably enhances accuracy by up to 12.97% while reducing latency by 51% compared to conventional RAG systems on PubHealth.
  arXiv  Detail & Related papers  (2024-07-11T06:50:19Z)
• Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection [28.15184715270483]
  Large language models (LLMs) augmented with retrieval exhibit robust performance and extensive versatility. We propose a novel paradigm named Sparse RAG, which seeks to cut costs through sparsity. Sparse RAG encodes retrieved documents in parallel, which eliminates latency introduced by long-range attention of retrieved documents.
  arXiv  Detail & Related papers  (2024-05-25T11:10:04Z)
• RaFe: Ranking Feedback Improves Query Rewriting for RAG [83.24385658573198]
  We propose a framework for training query rewriting models free of annotations. By leveraging a publicly available reranker, oursprovides feedback aligned well with the rewriting objectives.
  arXiv  Detail & Related papers  (2024-05-23T11:00:19Z)
• Improving Retrieval for RAG based Question Answering Models on Financial Documents [0.046603287532620746]
  This paper explores the existing constraints of RAG pipelines and introduces methodologies for enhancing text retrieval. It delves into strategies such as sophisticated chunking techniques, query expansion, the incorporation of metadata annotations, the application of re-ranking algorithms, and the fine-tuning of embedding algorithms.
  arXiv  Detail & Related papers  (2024-03-23T00:49:40Z)
• Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers [0.0]
  Retrieval-Augmented Generation (RAG) is a prevalent approach to infuse a private knowledge base of documents with Large Language Models (LLM) to build Generative Q&A (Question-Answering) systems. We propose the 'Blended RAG' method of leveraging semantic search techniques, such as Vector indexes and Sparse indexes, blended with hybrid query strategies. Our study achieves better retrieval results and sets new benchmarks for IR (Information Retrieval) datasets like NQ and TREC-COVID datasets.
  arXiv  Detail & Related papers  (2024-03-22T17:13:46Z)
• Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation [128.01050030936028]
  We propose an information refinement training method named InFO-RAG. InFO-RAG is low-cost and general across various tasks. It improves the performance of LLaMA2 by an average of 9.39% relative points.
  arXiv  Detail & Related papers  (2024-02-28T08:24:38Z)
• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
  Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
  arXiv  Detail & Related papers  (2024-01-30T14:25:32Z)
• Corrective Retrieval Augmented Generation [36.04062963574603]
  Retrieval-augmented generation (RAG) relies heavily on relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong. We propose the Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches.
  arXiv  Detail & Related papers  (2024-01-29T04:36:39Z)

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.