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GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
GRIP represents a significant advancement in the adaptation of Large Language Models (LLMs) for structural data processing. While LLMs excel in sequential textual data, their application to complex structures like knowledge graphs has been limited due to the inefficiencies of existing methods, which often involve cumbersome post-training and alignment procedures. GRIP overcomes these hurdles by employing a novel approach that integrates in-parameter knowledge injection, enabling LLMs to internalize complex relational information efficiently. This is achieved through fine-tuning tasks that store knowledge in lightweight LoRA parameters, allowing for a more streamlined and effective handling of graph-related tasks. The effectiveness of GRIP has been validated through extensive experiments across multiple benchmarks, showcasing its potential to enhance LLM capabilities significantly.
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