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KAG: Knowledge Augmented Generation Framework (Liang et al., 2024)
KAG (Knowledge Augmented Generation) is a professional-domain LLM service framework introduced by Liang et al. (arXiv:2409.13731, 2024) and implemented on the open-source OpenSPG engine. KAG bidirectionally couples large language models with knowledge graphs through five components: (i) an LLM-friendly knowledge representation that aligns KG schemas with natural-language-friendly formats, (ii) mutual indexing between knowledge-graph nodes and the original text chunks they were extracted from, (iii) a logical-form-guided hybrid reasoning engine that interleaves symbolic logical-form steps with LLM calls and retrieval, (iv) knowledge alignment via semantic reasoning to reconcile entities and relations across sources, and (v) model capability enhancement that fine-tunes the LLM for KAG-style retrieval and reasoning. On multi-hop QA, KAG reports relative F1 gains of 19.6% on 2WikiMultiHopQA and 33.5% on HotpotQA over strong RAG baselines, and has been deployed on Ant Group's E-Government and E-Health QA. Within the Graph-RAG landscape, KAG is the canonical example of a structural / logical-form-guided Graph-RAG framework, in contrast with community-summary GraphRAG and dual-level LightRAG.
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Reference: QASC: A Dataset for Question Answering via Sentence Composition (arXiv preprint)
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Reference: ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
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Reference: Evaluation measures (information retrieval)
Reference: REPLUG: Retrieval-Augmented Black-Box Language Models
Reference: arxiv.org
Reference: HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Reference: HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering (arXiv preprint)
Reference: HotpotQA Official Dataset and Leaderboard
Reference: Dense Passage Retrieval for Open-Domain Question Answering
Reference: Dense Passage Retrieval for Open-Domain Question Answering (arXiv preprint)
LectureBank Dataset
MOOC-CS Prerequisite Benchmark
QASC Question Answering Benchmark
Late-Interaction Neural Retrieval
Recall@k Retrieval Metric
RePlug Retrieval-Augmented Black-Box Language Model
HotpotQA Multi-Hop QA Benchmark
Single-Vector Dense Passage Retrieval
Reference: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Reference: How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG
Reference: arxiv.org
Unbiased GraphRAG Evaluation Framework (Zeng et al., 2025)
Reference: RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
Reference: arxiv.org
RAG vs Graph-RAG Controlled Comparison (Han et al., 2025)
Reference: Controlled Retrieval-augmented Context Evaluation for Long-form RAG
Reference: Controlled Retrieval-augmented Context Evaluation for Long-form RAG (ACL Anthology)
CRUX Controlled RAG Context Evaluation (Ju et al., 2025)
Reference: Anytime Heuristic Search
Reference: The Anatomy of a Large-Scale Hypertextual Web Search Engine