RManLuo/graph-constrained-reasoning
Official Implementation of ICML 2025 Paper: "Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models".
This framework helps AI developers integrate structured knowledge from existing knowledge graphs with the flexible reasoning capabilities of large language models. It takes a knowledge graph and a large language model as input, ensuring the LLM generates accurate, verifiable reasoning paths and answers directly grounded in the graph's facts, eliminating hallucinations. The primary users are AI engineers or researchers building question-answering systems or knowledge-driven AI applications.
238 stars. No commits in the last 6 months.
Use this if you need to build AI systems that provide accurate, verifiable answers by reasoning directly over a knowledge graph, preventing the LLM from generating false or unsupported information.
Not ideal if your application does not involve structured knowledge graphs or if you do not require strict factual grounding for LLM outputs.
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238
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25
Language
Python
License
MIT
Category
Last pushed
May 20, 2025
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