Explore-on-Graph: Incentivizing Autonomous Exploration of Large Language Models on Knowledge Graphs with Path-refined Reward Modeling
arXiv:2602.21728v1 Announce Type: new Abstract: The reasoning process of Large Language Models (LLMs) is often plagued by hallucinations and missing facts in question-answering tasks. A promising solution is to ground LLMs' answers in verifiable knowledge sources, such as Knowledge Graphs (KGs). Prevailing KG-enhanced methods typically constrained LLM reasoning either by enforcing rules during generation or by imitating paths from a fixed set of demonstrations. However, they naturally confined the reasoning patterns of LLMs within the scope of prior experience or fine-tuning data, limiting their generalizability to out-of-distribution graph reasoning problems. To tackle this problem, in this paper, we propose Explore-on-Graph (EoG), a novel framework that encourages LLMs to autonomously explore a more diverse reasoning space on KGs. To incentivize exploration and discovery of novel reasoning paths, we propose to introduce reinforcement learning during training, whose reward is the cor
arXiv:2602.21728v1 Announce Type: new Abstract: The reasoning process of Large Language Models (LLMs) is often plagued by hallucinations and missing facts in question-answering tasks. A promising solution is to ground LLMs' answers in verifiable knowledge sources, such as Knowledge Graphs (KGs). Prevailing KG-enhanced methods typically constrained LLM reasoning either by enforcing rules during generation or by imitating paths from a fixed set of demonstrations. However, they naturally confined the reasoning patterns of LLMs within the scope of prior experience or fine-tuning data, limiting their generalizability to out-of-distribution graph reasoning problems. To tackle this problem, in this paper, we propose Explore-on-Graph (EoG), a novel framework that encourages LLMs to autonomously explore a more diverse reasoning space on KGs. To incentivize exploration and discovery of novel reasoning paths, we propose to introduce reinforcement learning during training, whose reward is the correctness of the reasoning paths' final answers. To enhance the efficiency and meaningfulness of the exploration, we propose to incorporate path information as additional reward signals to refine the exploration process and reduce futile efforts. Extensive experiments on five KGQA benchmark datasets demonstrate that, to the best of our knowledge, our method achieves state-of-the-art performance, outperforming not only open-source but also even closed-source LLMs.
Executive Summary
The article 'Explore-on-Graph: Incentivizing Autonomous Exploration of Large Language Models on Knowledge Graphs with Path-refined Reward Modeling' introduces a novel framework, Explore-on-Graph (EoG), designed to enhance the reasoning capabilities of Large Language Models (LLMs) by leveraging Knowledge Graphs (KGs). The framework addresses the issue of hallucinations and missing facts in LLM reasoning by encouraging autonomous exploration of diverse reasoning paths. Through reinforcement learning and path-refined reward modeling, EoG aims to improve the efficiency and meaningfulness of the exploration process. The study demonstrates state-of-the-art performance on five KGQA benchmark datasets, outperforming both open-source and closed-source LLMs.
Key Points
- ▸ Introduction of the Explore-on-Graph (EoG) framework for enhancing LLM reasoning on Knowledge Graphs.
- ▸ Use of reinforcement learning to incentivize exploration and discovery of novel reasoning paths.
- ▸ Incorporation of path information as additional reward signals to refine the exploration process.
- ▸ State-of-the-art performance on five KGQA benchmark datasets.
Merits
Innovative Framework
The EoG framework represents a significant advancement in the field of LLM reasoning, addressing the critical issue of hallucinations and missing facts by leveraging Knowledge Graphs.
Effective Use of Reinforcement Learning
The incorporation of reinforcement learning to incentivize exploration and discovery of novel reasoning paths is a novel approach that enhances the efficiency and meaningfulness of the exploration process.
State-of-the-Art Performance
The framework achieves state-of-the-art performance on five KGQA benchmark datasets, demonstrating its effectiveness and potential for real-world applications.
Demerits
Generalizability Concerns
While the framework shows promising results, there may be concerns about its generalizability to other types of reasoning tasks or datasets outside the scope of the studied benchmarks.
Computational Resources
The use of reinforcement learning and path-refined reward modeling may require significant computational resources, which could limit its accessibility and practical implementation.
Expert Commentary
The article presents a significant contribution to the field of LLM reasoning by introducing the Explore-on-Graph (EoG) framework. The use of reinforcement learning to incentivize exploration and discovery of novel reasoning paths is a novel and innovative approach. The framework's ability to achieve state-of-the-art performance on five KGQA benchmark datasets is particularly noteworthy. However, there are concerns about the generalizability of the framework to other types of reasoning tasks and the computational resources required for its implementation. The article's findings have important implications for both practical applications and policy decisions related to AI systems. Overall, the study represents a valuable addition to the ongoing discourse on enhancing the reasoning capabilities of LLMs.
Recommendations
- ✓ Further research should be conducted to evaluate the generalizability of the EoG framework to other types of reasoning tasks and datasets.
- ✓ Future studies should explore ways to optimize the computational resources required for the implementation of the EoG framework to enhance its accessibility and practical implementation.
