Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models (2410.13080v1)

Abstract: LLMs have demonstrated impressive reasoning abilities, but they still struggle with faithful reasoning due to knowledge gaps and hallucinations. To address these issues, knowledge graphs (KGs) have been utilized to enhance LLM reasoning through their structured knowledge. However, existing KG-enhanced methods, either retrieval-based or agent-based, encounter difficulties in accurately retrieving knowledge and efficiently traversing KGs at scale. In this work, we introduce graph-constrained reasoning (GCR), a novel framework that bridges structured knowledge in KGs with unstructured reasoning in LLMs. To eliminate hallucinations, GCR ensures faithful KG-grounded reasoning by integrating KG structure into the LLM decoding process through KG-Trie, a trie-based index that encodes KG reasoning paths. KG-Trie constrains the decoding process, allowing LLMs to directly reason on graphs and generate faithful reasoning paths grounded in KGs. Additionally, GCR leverages a lightweight KG-specialized LLM for graph-constrained reasoning alongside a powerful general LLM for inductive reasoning over multiple reasoning paths, resulting in accurate reasoning with zero reasoning hallucination. Extensive experiments on several KGQA benchmarks demonstrate that GCR achieves state-of-the-art performance and exhibits strong zero-shot generalizability to unseen KGs without additional training.

Graph-Constrained Reasoning: A Structured Approach to Enhance LLMs with KGs

The integration of structured knowledge within the unstructured reasoning capabilities of LLMs is a pivotal challenge in artificial intelligence research. The paper “Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with LLMs” introduces a methodology aptly named Graph-Constrained Reasoning (GCR), which targets this integration by eliminating reasoning hallucinations while harnessing the full potential of LLMs. This innovative approach seeks to leverage the structured nature of Knowledge Graphs (KGs) to enhance LLM reasoning capabilities in a faithful and efficient manner.

Key Contributions

1. Graph-Constrained Decoding with KG-Trie: The heart of the GCR framework is the KG-Trie, a trie-based index that encodes reasoning paths within a knowledge graph. This structural index serves as a guiding constraint during the LLM decoding process, only permitting reasoning paths that are legitimate within the KG, thereby ensuring the faithfulness of the generated output. By constraining the output space of LLMs, GCR emphasizes generating reasoning paths that are both realistic and plausible within the contextual boundaries set by the KG.
2. Combination of Lightweight and General LLMs: GCR tactically employs a lightweight KG-specialized LLM for reasoning directly on the KG. This LLM is fine-tuned specifically for the task of generating potential reasoning paths and hypothesis answers under the constraints of the KG-Trie. Subsequently, a more powerful general LLM evaluates these multiple paths to produce the final answers by leveraging its broader inductive reasoning capability.
3. Zero-Hallucination and Generalizability: Extensive experiments on benchmark KGQA datasets such as WebQSP and CWQ demonstrate GCR's ability to achieve state-of-the-art performance, notable for its zero hallucination in reasoning and strong zero-shot generalizability to unseen KGs. This capability underscores GCR's adaptability and robust performances across diverse datasets without necessitating additional training.

Numerical Achievements

GCR exceeds previous benchmarks in KGQA tasks, with notable improvements in metrics such as Hit and F1 scores across datasets. Notably, GCR achieves a 92.6% hit rate on WebQSP and a 75.8% on CWQ, outperforming existing methods like GNN-RAG and RoG in both accuracy and efficiency. This performance is achieved while maintaining reasonable runtime efficiency, demonstrating the effectiveness of KG-Trie constraints in optimizing the balance between computational cost and performance.

Implications and Future Directions

Practically, GCR provides a viable pathway for deploying LLMs in applications requiring high reliability and domain-specific knowledge integration, such as medical data analysis and legal advisory systems. Theoretical advancements from this work could inspire further research into hybrid frameworks that blend structured and unstructured data reasoning, potentially influencing fields such as cognitive computing and semantic web development.

Future research directions may explore the extension of GCR to more complex and dynamic KG structures or the integration with multimodal datasets, which require reasoning beyond textual data. Additionally, investigating the scalability of KG-Trie construction and its performance on extremely large KGs would be valuable for deploying these models in real-world applications with extensive datasets.

In conclusion, the graph-constrained reasoning framework presented represents a significant advancement in leveraging both the structured robustness of KGs and the expansive potential of LLMs, pushing the boundaries of faithful AI reasoning capabilities.