Meta KDD杯冠军团队打造高效RAG系统，三任务全胜。
Winning Solution For Meta KDD Cup' 24
- 用调优大模型提升网页与知识图谱信息抽取能力
- 三任务得分分别为28.4%、42.7%、47.8%，均排名第一
- 适合关注真实场景下RAG系统构建的研究者
本文介绍db3团队在Meta KDD Cup 24中的全部任务获胜方案。挑战要求从网络来源和知识图谱构建RAG系统，每个问题提供多个数据源。比赛包含三个任务：(1) 将网页信息精炼为准确答案；(2) 整合模拟知识图谱的结构化数据；(3) 从大量网页和API中筛选并整合关键信息以应对真实检索挑战。任务一采用网页或开放数据检索与问答框架，对大语言模型（LLM）进行微调以增强RAG性能并减少幻觉。任务二与任务三基于正则化API集合和调优后LLM生成的API方法，知识图谱接口直接提取相关数据辅助模型正确回答。最终方案在三项任务中均获第一，得分分别为28.4%、42.7%和47.8%。
原文摘要 · Abstract (English)
This paper describes the winning solutions of all tasks in Meta KDD Cup 24 from db3 team. The challenge is to build a RAG system from web sources and knowledge graphs. We are given multiple sources for each query to help us answer the question. The CRAG challenge involves three tasks: (1) condensing information from web pages into accurate answers, (2) integrating structured data from mock knowledge graphs, and (3) selecting and integrating critical data from extensive web pages and APIs to reflect real-world retrieval challenges. Our solution for Task #1 is a framework of web or open-data retrieval and answering. The large language model (LLM) is tuned for better RAG performance and less hallucination. Task #2 and Task #3 solutions are based on a regularized API set for domain questions and the API generation method using tuned LLM. Our knowledge graph API interface extracts directly relevant information to help LLMs answer correctly. Our solution achieves 1st place on all three tasks, achieving a score of 28.4%, 42.7%, and 47.8%, respectively.
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