Revisiting the Solution of Meta KDD Cup 2024: CRAG

Jie Ouyang, Yucong Luo, Mingyue Cheng, Daoyu Wang, Shuo Yu +2 more
9/9/2024
cs.IRcs.AIcs.CL

Official code: USTCAGI/CRAG-in-KDD-Cup2024 (60 stars, last commit 2025-01-19).

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.

View on arXivView PDF

Code Implementations(1)

606PythonJul 12, 20241 years ago

Similar Papers

Cite this paper

@article{ouyang2024revisiting,
  title  = {Revisiting the Solution of Meta KDD Cup 2024: CRAG},
  author = {Jie Ouyang and Yucong Luo and Mingyue Cheng and Daoyu Wang and Shuo Yu and Qi Liu and Enhong Chen},
  year   = {2024},
  eprint = {2409.15337},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.15337v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2409.15337.svg)](https://codeofpaper.com/paper/2409.15337)

Discussion