Revisiting the Solution of Meta KDD Cup 2024: CRAG
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
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
- 2023Unlocking the Potential of Large Language Models for Explainable Recommendationscited by 18
- 2024Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platformcited by 10
- 2023Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modelingcited by 4
Built on by
Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
- 2025HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generationcited by 13
- 2025AssoMem: Scalable Memory QA with Multi-Signal Associative Retrievalcited by 10
- 2024Leveraging LLMs for Hypothetical Deduction in Logical Inference: A Neuro-Symbolic Approachcited by 6
- 2025KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answeringcited by 5
- 2026CacheRAG: A Semantic Caching System for Retrieval-Augmented Generation in Knowledge Graph Question Answeringcited by 0
- 2026CacheRAG: A Semantic Caching System for Retrieval-Augmented Generation in Knowledge Graph Question Answering28 Apr 2026
- 2025AssoMem: Scalable Memory QA with Multi-Signal Associative Retrieval12 Oct 2025
- 2025KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering5 Sep 2025
- 2025HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation3 Mar 2025
- 2024Leveraging LLMs for Hypothetical Deduction in Logical Inference: A Neuro-Symbolic Approach29 Oct 2024
Similar, not cited
Close in wording, no citation either way
- 2024CRAG -- Comprehensive RAG Benchmark
- 2024MARAGS: A Multi-Adapter System for Multi-Task Retrieval Augmented Generation Question Answering
- 2024Winning Solution For Meta KDD Cup' 24
- 2025LiveRAG: A diverse Q&A dataset with varying difficulty level for RAG evaluation
- 2024A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning
- 2024R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation
- 2025PreQRAG -- Classify and Rewrite for Enhanced RAG
- 2026RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routing