Papers
Topics
Authors
Recent
Search
2000 character limit reached

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

Published 24 Oct 2024 in cs.CL | (2410.18415v1)

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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (49)
  1. Gpt-4 technical report.
  2. Self-RAG: Learning to retrieve, generate, and critique through self-reflection. In The Twelfth International Conference on Learning Representations.
  3. Knowledge-augmented language model prompting for zero-shot knowledge graph question answering. In Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE), pages 78–106, Toronto, Canada. Association for Computational Linguistics.
  4. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD ’08, page 1247–1250, New York, NY, USA. Association for Computing Machinery.
  5. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pages 1877–1901. Curran Associates, Inc.
  6. Large language models can better understand knowledge graphs than we thought.
  7. The llama 3 herd of models. Preprint, arXiv:2407.21783.
  8. Trace the evidence: Constructing knowledge-grounded reasoning chains for retrieval-augmented generation. ArXiv, abs/2406.11460.
  9. Towards revealing the mystery behind chain of thought: A theoretical perspective. In Thirty-seventh Conference on Neural Information Processing Systems.
  10. Improving multi-hop knowledge base question answering by learning intermediate supervision signals. In WSDM.
  11. G-retriever: Retrieval-augmented generation for textual graph understanding and question answering. Preprint, arXiv:2402.07630.
  12. Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6609–6625, Barcelona, Spain (Online). International Committee on Computational Linguistics.
  13. The curious case of neural text degeneration. In International Conference on Learning Representations.
  14. Faithful question answering with Monte-Carlo planning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3944–3965, Toronto, Canada. Association for Computational Linguistics.
  15. StructGPT: A general framework for large language model to reason over structured data. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 9237–9251, Singapore. Association for Computational Linguistics.
  16. Large language models on graphs: A comprehensive survey. IEEE Transactions on Knowledge and Data Engineering, pages 1–20.
  17. Graph chain-of-thought: Augmenting large language models by reasoning on graphs. In Findings of the Association for Computational Linguistics ACL 2024, pages 163–184, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
  18. Complex knowledge base question answering: A survey. Preprint, arXiv:2108.06688.
  19. Yunshi Lan and Jing Jiang. 2020. Query graph generation for answering multi-hop complex questions from knowledge bases. In Annual Meeting of the Association for Computational Linguistics.
  20. Retrieval-augmented generation for knowledge-intensive nlp tasks. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS ’20, Red Hook, NY, USA. Curran Associates Inc.
  21. Grounded dialogue generation with cross-encoding re-ranker, grounding span prediction, and passage dropout. In Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering, pages 123–129, Dublin, Ireland. Association for Computational Linguistics.
  22. Ruosen Li and Xinya Du. 2023. Leveraging structured information for explainable multi-hop question answering and reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6779–6789, Singapore. Association for Computational Linguistics.
  23. Reasoning on graphs: Faithful and interpretable large language model reasoning. In The Twelfth International Conference on Learning Representations.
  24. Tree-of-traversals: A zero-shot reasoning algorithm for augmenting black-box language models with knowledge graphs. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12302–12319, Bangkok, Thailand. Association for Computational Linguistics.
  25. Costas Mavromatis and George Karypis. 2024. Gnn-rag: Graph neural retrieval for large language model reasoning. Preprint, arXiv:2405.20139.
  26. ReLiK: Retrieve and LinK, fast and accurate entity linking and relation extraction on an academic budget. In Findings of the Association for Computational Linguistics ACL 2024, pages 14114–14132, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
  27. Training language models to follow instructions with human feedback. Preprint, arXiv:2203.02155.
  28. Unifying large language models and knowledge graphs: A roadmap. IEEE Transactions on Knowledge and Data Engineering, 36(7):3580–3599.
  29. Qwen. 2024. Qwen2.5: A party of foundation models.
  30. A survey of large language models for graphs. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD ’24, page 6616–6626, New York, NY, USA. Association for Computing Machinery.
  31. Gemma 2: Improving open language models at a practical size. ArXiv, abs/2408.00118.
  32. Comprehensive analysis of freebase and dataset creation for robust evaluation of knowledge graph link prediction models. In The Semantic Web – ISWC 2023, pages 113–133, Cham. Springer Nature Switzerland.
  33. Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph. In The Twelfth International Conference on Learning Representations.
  34. ODA: Observation-driven agent for integrating LLMs and knowledge graphs. In Findings of the Association for Computational Linguistics ACL 2024, pages 7417–7431, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
  35. Sparqa: Skeleton-based semantic parsing for complex questions over knowledge bases. In AAAI Conference on Artificial Intelligence.
  36. Alon Talmor and Jonathan Berant. 2018. The web as a knowledge-base for answering complex questions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 641–651, New Orleans, Louisiana. Association for Computational Linguistics.
  37. Denny Vrandevcic and Markus Krotzsch. 2014. Wikidata. Communications of the ACM, 57:78–85.
  38. Knowledge-driven cot: Exploring faithful reasoning in llms for knowledge-intensive question answering. Preprint, arXiv:2308.13259.
  39. Xuezhi Wang and Denny Zhou. 2024. Chain-of-thought reasoning without prompting. ArXiv, abs/2402.10200.
  40. Finetuned language models are zero-shot learners. ArXiv, abs/2109.01652.
  41. Chain of thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems.
  42. Huggingface’s transformers: State-of-the-art natural language processing. Preprint, arXiv:1910.03771.
  43. Retrieve-rewrite-answer: A kg-to-text enhanced llms framework for knowledge graph question answering. Preprint, arXiv:2309.11206.
  44. Tree of thoughts: Deliberate problem solving with large language models. In Thirty-seventh Conference on Neural Information Processing Systems.
  45. RNG-KBQA: Generation augmented iterative ranking for knowledge base question answering. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6032–6043, Dublin, Ireland. Association for Computational Linguistics.
  46. The value of semantic parse labeling for knowledge base question answering. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 201–206, Berlin, Germany. Association for Computational Linguistics.
  47. Do large language models know what they don’t know? In Findings of the Association for Computational Linguistics: ACL 2023, pages 8653–8665, Toronto, Canada. Association for Computational Linguistics.
  48. DecAF: Joint decoding of answers and logical forms for question answering over knowledge bases. In The Eleventh International Conference on Learning Representations.
  49. StructLM: Towards building generalist models for structured knowledge grounding. In First Conference on Language Modeling.

Summary

  • The paper introduces well-formed chains that align LLM outputs with knowledge graph topology to ensure faithful and sound reasoning.
  • It employs a novel graph-aware constrained decoding method that dynamically expands local subgraphs without requiring additional training.
  • Experimental results on KGQA benchmarks show that DoG outperforms iterative prompting methods, enhancing accuracy in multi-hop question answering.

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.

Paper to Video (Beta)

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Tweets

Sign up for free to view the 1 tweet with 0 likes about this paper.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up for Free

HackerNews

  1. Faithful and Sound Reasoning on Knowledge Graphs (2 points, 1 comment)