Generalization of LLMs’ Trained Graph Knowledge to Actual Graph Reasoning

Determine whether large language models that are trained or fine-tuned on graph reasoning tasks can apply the learned graph knowledge and algorithms to solve actual graph reasoning problems.

Background

The paper discusses that many existing approaches train GNNs or fine-tune LLMs on specific graph reasoning tasks, but performance often degrades when transferring to other tasks. Retraining or fine-tuning for new tasks is resource-intensive.

The authors explicitly note uncertainty about whether LLMs can translate what they learn during training into effective solutions for actual graph reasoning tasks. Their case study further shows overfitting in a fine-tuned model (GraphWiz), which misclassifies a real-world webpage importance problem and fails to generate correct reasoning paths, highlighting the open question of generalization beyond training distributions.

References

Whether LLMs can apply the graph knowledge and algorithms learned during the training process to actual graph reasoning also remains an open question.

— Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents  (2410.05130 - Hu et al., 2024) in Section "Limitations of Single LLM in Graph Reasoning" (paragraph: A single LLM struggles to solve reasoning problems in real-world scenarios)

Relatedly, the dependence on few-shot exemplars noted above means our approach, while fine-tuning-free, is not yet exemplar-free, and its applicability to KGs lacking curated question-query pairs remains open.

— Enabling Knowledge Graph Understanding at Scale with the EXplore Your Graphs ENgine (EXYGEN)  (2609.11569 - Singh et al., 10 Sep 2026) in Section Conclusions and future work