Revisiting the Solution of Meta KDD Cup 2024: CRAG

• URL: http://arxiv.org/abs/2409.15337v1
• Date: Mon, 9 Sep 2024 07:28:14 GMT
• Title: Revisiting the Solution of Meta KDD Cup 2024: CRAG
• Authors: Jie Ouyang, Yucong Luo, Mingyue Cheng, Daoyu Wang, Shuo Yu, Qi Liu, Enhong Chen,
• Abstract summary: This paper presents the solution of our team APEX in the Meta KDD CUP 2024: CRAG Comprehensive RAG Benchmark Challenge. The CRAG benchmark addresses the limitations of existing QA benchmarks in evaluating the diverse and dynamic challenges faced by Retrieval-Augmented Generation (RAG) systems. We propose a routing-based domain and dynamic adaptive RAG pipeline, which performs specific processing for the diverse and dynamic nature of the question.
• Score: 45.217913880930155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the solution of our team APEX in the Meta KDD CUP 2024: CRAG Comprehensive RAG Benchmark Challenge. The CRAG benchmark addresses the limitations of existing QA benchmarks in evaluating the diverse and dynamic challenges faced by Retrieval-Augmented Generation (RAG) systems. It provides a more comprehensive assessment of RAG performance and contributes to advancing research in this field. We propose a routing-based domain and dynamic adaptive RAG pipeline, which performs specific processing for the diverse and dynamic nature of the question in all three stages: retrieval, augmentation, and generation. Our method achieved superior performance on CRAG and ranked 2nd for Task 2&3 on the final competition leaderboard. Our implementation is available at this link: https://github.com/USTCAGI/CRAG-in-KDD-Cup2024.

Related papers

• RAG-Gym: Optimizing Reasoning and Search Agents with Process Supervision [43.50113345998687]
  We introduce RAG-Gym, a unified optimization framework that enhances information-seeking agents through fine-grained process supervision at each search step. We also propose ReSearch, a novel agent architecture that synergizes answer reasoning and search query generation within the RAG-Gym framework.
  arXiv  Detail & Related papers  (2025-02-19T18:56:03Z)
• HawkBench: Investigating Resilience of RAG Methods on Stratified Information-Seeking Tasks [50.871243190126826]
  HawkBench is a human-labeled, multi-domain benchmark designed to rigorously assess RAG performance. By stratifying tasks based on information-seeking behaviors, HawkBench provides a systematic evaluation of how well RAG systems adapt to diverse user needs.
  arXiv  Detail & Related papers  (2025-02-19T06:33:39Z)
• Chain-of-Retrieval Augmented Generation [72.06205327186069]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer. Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• Toward General Instruction-Following Alignment for Retrieval-Augmented Generation [63.611024451010316]
  Following natural instructions is crucial for the effective application of Retrieval-Augmented Generation (RAG) systems. We propose VIF-RAG, the first automated, scalable, and verifiable synthetic pipeline for instruction-following alignment in RAG systems.
  arXiv  Detail & Related papers  (2024-10-12T16:30:51Z)
• MARAGS: A Multi-Adapter System for Multi-Task Retrieval Augmented Generation Question Answering [0.43512163406552007]
  We present a multi-adapter retrieval augmented generation system (MARAGS) for Meta's Comprehensive RAG (CRAG) competition for KDD CUP 2024. Our system achieved 2nd place for Task 1 as well as 3rd place on Task 2.
  arXiv  Detail & Related papers  (2024-09-05T01:58:29Z)
• Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track [51.25144287084172]
  It is crucial to have an arena to build, test, visualize, and systematically evaluate RAG-based search systems. We propose the TREC 2024 RAG Track to foster innovation in evaluating RAG systems.
  arXiv  Detail & Related papers  (2024-06-24T17:37:52Z)
• CRAG -- Comprehensive RAG Benchmark [58.15980697921195]
  Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets do not adequately represent the diverse and dynamic nature of real-world Question Answering (QA) tasks. To bridge this gap, we introduce the Comprehensive RAG Benchmark (CRAG) CRAG is a factual question answering benchmark of 4,409 question-answer pairs and mock APIs to simulate web and Knowledge Graph (KG) search.
  arXiv  Detail & Related papers  (2024-06-07T08:43:07Z)
• Retrieval-Augmented Generation for AI-Generated Content: A Survey [38.50754568320154]
  Retrieval-Augmented Generation (RAG) has emerged as a paradigm to address such challenges. RAG introduces the information retrieval process, which enhances the generation process by retrieving relevant objects from available data stores. In this paper, we comprehensively review existing efforts that integrate RAG technique into AIGC scenarios.
  arXiv  Detail & Related papers  (2024-02-29T18:59:01Z)

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.