LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph
- Published
- Source
- arXiv
- Paper number
- 057
- Field
- Knowledge Graphs / LLMs
- arXiv ID
- 2504.03137
Key points
- Existing KG-enhanced LLM approaches rely on large, computationally expensive models and complex KG traversal strategies.
- Current methods do not exploit structural information in knowledge graphs effectively, which leads to redundant information and inaccurate reasoning.
- It is a three-module framework that combines reasoning-graph retrieval, knowledge embedding through a small Transformer adapter, and mixed-prompt reasoning.
- It is a parameter-efficient training method that freezes the LLM parameters and updates only the Knowledge Adapter.
- It improves knowledge integration by converting both text content and graph structure into embeddings that are used as soft prompts.
- It uses constrained breadth-first search with LLM-based scoring to retrieve relevant reasoning paths in the knowledge graph.
Paper links
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