Injecting Knowledge Graphs into Large Language Models
This addresses the problem of enhancing symbolic reasoning in LLMs for applications requiring structured knowledge, representing an incremental advancement over existing integration techniques.
The paper tackles the challenge of integrating structured knowledge from Knowledge Graphs into Large Language Models for symbolic reasoning, achieving improved reasoning performance with a resource-efficient, model-agnostic approach that shows the best trade-off in accuracy and efficiency against state-of-the-art methods.
Integrating structured knowledge from Knowledge Graphs (KGs) into Large Language Models (LLMs) remains a key challenge for symbolic reasoning. Existing methods mainly rely on prompt engineering or fine-tuning, which lose structural fidelity or incur high computational costs. Building on recent encoding techniques which integrate graph embeddings within the LLM input as tokens, we extend this paradigm to the KG domain by leveraging Knowledge Graph Embedding (KGE) models, thus enabling graph-aware reasoning. Our approach is model-agnostic, resource-efficient, and compatible with any LLMs. Extensive experimentation on synthetic and real-world datasets shows that our method improves reasoning performance over established baselines, further achieving the best trade-off in terms of accuracy and efficiency against state-of-the-art LLMs.