mlsanityData to 14 Oct 2026

DB3 Team's Solution For Meta KDD Cup' 25

arXiv 2509.09681 ↗First posted 12 Aug 2025cs.IR cs.AI cs.CLcited by 2

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Abstract (arXiv)

This paper presents the db3 team's winning solution for the Meta CRAG-MM Challenge 2025 at KDD Cup'25. Addressing the challenge's unique multi-modal, multi-turn question answering benchmark (CRAG-MM), we developed a comprehensive framework that integrates tailored retrieval pipelines for different tasks with a unified LLM-tuning approach for hallucination control. Our solution features (1) domain-specific retrieval pipelines handling image-indexed knowledge graphs, web sources, and multi-turn conversations; and (2) advanced refusal training using SFT, DPO, and RL. The system achieved 2nd place in Task 1, 2nd place in Task 2, and 1st place in Task 3, securing the grand prize for excellence in ego-centric queries through superior handling of first-person perspective challenges.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 8 references that are in the map. First posted on arXiv 12 Aug 2025.

Builds on

4 of 8 references.

References · 4

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  4. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025CRAG-MM: Multi-modal Multi-turn Comprehensive RAG Benchmarkcited by 10
  2. 2025Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understandingcited by 9

Similar, not cited

Close in wording, no citation either way

  1. 2024Winning Solution For Meta KDD Cup' 24
  2. 2025Ensembling Multiple Hallucination Detectors Trained on VLLM Internal Representations ↗
  3. 2025Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering ↗
  4. 2025Multi-Stage Verification-Centric Framework for Mitigating Hallucination in Multi-Modal RAG
  5. 2023A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models
  6. 2025Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysis
  7. 2026Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)
  8. 2025LettuceDetect: A Hallucination Detection Framework for RAG Applications