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Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains (2410.18415v1)

Published 24 Oct 2024 in cs.CL

Abstract: Knowledge Graphs (KGs) can serve as reliable knowledge sources for question answering (QA) due to their structured representation of knowledge. Existing research on the utilization of KG for LLMs prevalently relies on subgraph retriever or iterative prompting, overlooking the potential synergy of LLMs' step-wise reasoning capabilities and KGs' structural nature. In this paper, we present DoG (Decoding on Graphs), a novel framework that facilitates a deep synergy between LLMs and KGs. We first define a concept, well-formed chain, which consists of a sequence of interrelated fact triplets on the KGs, starting from question entities and leading to answers. We argue that this concept can serve as a principle for making faithful and sound reasoning for KGQA. To enable LLMs to generate well-formed chains, we propose graph-aware constrained decoding, in which a constraint derived from the topology of the KG regulates the decoding process of the LLMs. This constrained decoding method ensures the generation of well-formed chains while making full use of the step-wise reasoning capabilities of LLMs. Based on the above, DoG, a training-free approach, is able to provide faithful and sound reasoning trajectories grounded on the KGs. Experiments across various KGQA tasks with different background KGs demonstrate that DoG achieves superior and robust performance. DoG also shows general applicability with various open-source LLMs.

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Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains

The research paper "Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains" presents a novel approach, termed DoG, that aims to enhance the synergy between Knowledge Graphs (KGs) and LLMs in the field of Knowledge Graph Question Answering (KGQA). The researchers identify limitations in existing methods that rely heavily on subgraph retrievers or iterative LLM prompting and propose a framework that leverages the inherent reasoning capabilities of LLMs in conjunction with the structural information provided by KGs.

Key Contributions

  1. Well-Formed Chains: The paper introduces the concept of well-formed chains, which are sequences of fact triplets on KGs that lead from the entities pertinent to the question to the answer. This concept ensures that the reasoning process is both faithful and sound, adhering closely to the graph's topology.
  2. Graph-Aware Constrained Decoding: To enable LLMs to generate well-formed chains, the authors propose a graph-aware constrained decoding mechanism. This method applies constraints on the LLM's decoding process, influenced by the KG's topology. As the reasoning unfolds, the local subgraph expands dynamically, ensuring that only structurally coherent sequences are produced.
  3. Training-Free Approach: Unlike some methodologies that require extensive training of subgraph retrievers or involve modifying LLMs through specialized training, DoG remains training-free, thus preserving the LLM's pre-trained capabilities while effectively grounding its reasoning in the KG's structure.

Experimental Evaluation

The authors conduct experiments on three widely used KGQA benchmarks: WebQuestionSP, Complex WebQuestion, and 2Wikimultihop, each featuring different background KGs. The results indicate that DoG consistently surpasses various existing methods, including those based on iterative prompting and specialized retrievers. The incorporation of beam search in DoG enhances its performance, particularly on datasets involving multi-hop questions, by maintaining multiple reasoning paths.

Implications and Future Directions

The implications of this research are multifaceted. The robust framework of DoG suggests potential applications beyond KGQA, possibly extending to areas like automated reasoning and enhanced LLM interpretability. Future work could explore the integration of DoG with neural-symbolic systems or investigate its applicability across a broader range of languages and domains.

In conclusion, this paper contributes a significant advancement to the field by effectively bridging the reasoning capabilities of LLMs with the structured nature of KGs through a novel, constraint-driven decoding approach. This seamlessly integrated method offers a promising avenue for developing more accurate and coherent reasoning systems that require minimal domain-specific adaptations.

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Authors (6)
  1. Kun Li (192 papers)
  2. Tianhua Zhang (10 papers)
  3. Xixin Wu (85 papers)
  4. Hongyin Luo (31 papers)
  5. James Glass (173 papers)
  6. Helen Meng (204 papers)
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