mlsanityData to 7 Oct 2026

Revisiting the Solution of Meta KDD Cup 2024: CRAG

arXiv 2409.15337 ↗First posted 9 Sep 2024cs.IR cs.AI cs.CLcited by 5

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 5 of its 12 references that are in the map. First posted on arXiv 9 Sep 2024.

Builds on

5 of 12 references.

References · 5

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2023Unlocking the Potential of Large Language Models for Explainable Recommendationscited by 18
  4. 2024Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platformcited by 10
  5. 2023Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modelingcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024CRAG -- Comprehensive RAG Benchmark
  2. 2024MARAGS: A Multi-Adapter System for Multi-Task Retrieval Augmented Generation Question Answering
  3. 2024Winning Solution For Meta KDD Cup' 24
  4. 2025LiveRAG: A diverse Q&A dataset with varying difficulty level for RAG evaluation
  5. 2024A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning
  6. 2024R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation
  7. 2025PreQRAG -- Classify and Rewrite for Enhanced RAG
  8. 2026RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routing