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Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering
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Despite their competitive performance on knowledge-intensive tasks, large language models (LLMs) still have limitations in memorizing all world knowledge especially long tail knowledge. In this paper, we study the KG-augmented language model approach for solving the knowledge graph question answering (KGQA) task that requires rich world knowledge. Existing work has shown that retrieving KG knowledge to enhance LLMs prompting can significantly improve LLMs performance in KGQA. However, their approaches lack a well-formed verbalization of KG knowledge, i.e., they ignore the gap between KG representations and textual representations. To this end, we propose an answer-sensitive KG-to-Text approach that can transform KG knowledge into well-textualized statements most informative for KGQA. Based on this approach, we propose a KG-to-Text enhanced LLMs framework for solving the KGQA task. Experiments on several KGQA benchmarks show that the proposed KG-to-Text augmented LLMs approach outperforms previous KG-augmented LLMs approaches regarding answer accuracy and usefulness of knowledge statements.
Forward citations
Cited by 7 Pith papers
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Improving Factuality for Dialogue Response Generation via Graph-Based Knowledge Augmentation
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Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks
Offline walks over a knowledge graph, verbalized into text and retrieved by embedding similarity, let a single LLM call answer multi-hop questions competitively without any fine-tuning.
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KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering
KG2Code-QA represents knowledge-graph subgraphs as executable Python/NetworkX code and trains LLMs to complete the code, improving KGQA accuracy and cross-KG transfer under oracle retrieval.
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Fusing Knowledge and Language: A Comparative Study of Knowledge Graph-Based Question Answering with LLMs
In a small comparative study, GraphRAG outscored spaCy and CoreNLP-based KG-QA pipelines on reasoning-heavy questions, but the evaluation design conflates method choice with pipeline architecture.
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DistRAG: Towards Distance-Based Spatial Reasoning in LLMs
Retrieving distance facts from a spatial graph improves LLM answers to direct and nearest-city distance questions, while complex distance-comparison questions remain unsolved.
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An LLM merges three biomedical ontologies into a small knowledge graph, but its validation metrics are self-contradictory and the resource is not released.
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LLM Inference Enhanced by External Knowledge: A Survey
A survey of methods that enhance LLM inference by integrating external structured knowledge from tables and knowledge graphs.
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