REVIEW 13 cited by
Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) addresses these issues by grounding LLM outputs in structured external knowledge from KGs. However, current KG-based RAG frameworks still struggle to optimize the trade-off between retrieval effectiveness and efficiency in identifying a suitable amount of relevant graph information for the LLM to digest. We introduce SubgraphRAG, extending the KG-based RAG framework that retrieves subgraphs and leverages LLMs for reasoning and answer prediction. Our approach innovatively integrates a lightweight multilayer perceptron with a parallel triple-scoring mechanism for efficient and flexible subgraph retrieval while encoding directional structural distances to enhance retrieval effectiveness. The size of retrieved subgraphs can be flexibly adjusted to match the query's need and the downstream LLM's capabilities. This design strikes a balance between model complexity and reasoning power, enabling scalable and generalizable retrieval processes. Notably, based on our retrieved subgraphs, smaller LLMs like Llama3.1-8B-Instruct deliver competitive results with explainable reasoning, while larger models like GPT-4o achieve state-of-the-art accuracy compared with previous baselines -- all without fine-tuning. Extensive evaluations on the WebQSP and CWQ benchmarks highlight SubgraphRAG's strengths in efficiency, accuracy, and reliability by reducing hallucinations and improving response grounding.
Forward citations
Cited by 13 Pith papers
-
KAMR: Grounding Generation via Knowledge-Aligned Multi-hop Retrieval
Partial-alignment contrastive pretraining plus anchor-then-expand graph retrieval improves multi-hop KG evidence recovery and downstream QA over strong dense and graph RAG baselines.
-
Query-Focused Event Summarization: A Dataset and Benchmark
QFESum provides a large event-oriented QFS benchmark; RAT adaptive retrieval plus SHC hierarchical event clustering beat baselines on lexical, semantic, LLM-event-match and human metrics.
-
Agents-K1: Towards Agent-native Knowledge Orchestration
Agents-K1 is an end-to-end pipeline with a multimodal parser, 4B GRPO-trained extractor, and agent CLI that builds scientific knowledge graphs from full papers and was run on 2.46 million documents to produce Scholar-KG.
-
NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering
NeuroSymActive combines soft-unification symbolic modules, a neural path evaluator, and Monte-Carlo-style active exploration to reach strong answer accuracy on KGQA benchmarks while cutting graph lookups and model cal...
-
A2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoning
A2RAG raises Recall@2 by +9.9/+11.8 on HotpotQA/2WikiMultiHopQA and cuts tokens and latency by ~50% versus IRCoT via adaptive, provenance-grounded graph retrieval.
-
BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering
BYOKG-RAG combines LLM-generated entities, paths, queries, and candidate answers with multiple graph retrieval tools to answer questions over custom knowledge graphs without training data.
-
Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation
ReG refines weak graph-retriever supervision with LLM-selected reasoning chains and reorganizes retrieved triples into coherent evidence chains, improving KGQA accuracy, data efficiency, and reasoning token efficiency.
-
Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
RAPL combines LLM-rationalized path labels, line graph transformation, and path-based decoding to improve graph retrieval for KGQA, reporting state-of-the-art results on WebQSP and CWQ.
-
Search-on-Graph: Iterative Informed Navigation for Large Language Model Reasoning on Knowledge Graphs
An LLM that iteratively inspects 1-hop neighbors of a knowledge-graph entity and chooses the next relation achieves state-of-the-art KGQA scores on six Freebase/Wikidata benchmarks without fine-tuning.
-
SlimRAG: Retrieval without Graphs via Entity-Aware Context Selection
SlimRAG shows that an entity-aware inverted index without graphs can match or beat graph-based RAG retrieval on HotpotQA while using far fewer index tokens.
-
GPR: Empowering Generation with Graph-Pretrained Retriever
GPR pretrains a two-tower retriever on knowledge graphs using LLM-generated questions from masked triplets and a soft-preference triplet loss, improving KGQA accuracy across datasets and LLMs.
-
Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE
KG-SMILE applies perturbation and linear regression to a knowledge graph to attribute which entities and relations drive a GraphRAG system's answers.
-
Large Language models for Time Series Analysis: Techniques, Applications, and Challenges
A review of LLM-based time series analysis that proposes several taxonomies, but is undermined by citation errors and a lack of systematic methodology.
Discussion (0). Sign in to comment.