Unifying Large Language Models and Knowledge Graphs: A Roadmap

• URL: http://arxiv.org/abs/2306.08302v3
• Date: Thu, 25 Jan 2024 00:48:34 GMT
• Title: Unifying Large Language Models and Knowledge Graphs: A Roadmap
• Authors: Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, Xindong Wu
• Abstract summary: Large language models (LLMs) are making new waves in the field of natural language processing and artificial intelligence. Knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge.
• Score: 61.824618473293725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, Knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolving by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to unify LLMs and KGs together and simultaneously leverage their advantages. In this article, we present a forward-looking roadmap for the unification of LLMs and KGs. Our roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs, which incorporate KGs during the pre-training and inference phases of LLMs, or for the purpose of enhancing understanding of the knowledge learned by LLMs; 2) LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding, completion, construction, graph-to-text generation, and question answering; and 3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a mutually beneficial way to enhance both LLMs and KGs for bidirectional reasoning driven by both data and knowledge. We review and summarize existing efforts within these three frameworks in our roadmap and pinpoint their future research directions.

Related papers

• Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains [66.55612528039894]
  Knowledge Graphs (KGs) can serve as reliable knowledge sources for question answering (QA) We present DoG (Decoding on Graphs), a novel framework that facilitates a deep synergy between LLMs and KGs. Experiments across various KGQA tasks with different background KGs demonstrate that DoG achieves superior and robust performance.
  arXiv  Detail & Related papers  (2024-10-24T04:01:40Z)
• Combining Knowledge Graphs and Large Language Models [4.991122366385628]
  Large language models (LLMs) show astonishing results in language understanding and generation. They still show some disadvantages, such as hallucinations and lack of domain-specific knowledge. These issues can be effectively mitigated by incorporating knowledge graphs (KGs) This work collected 28 papers outlining methods for KG-powered LLMs, LLM-based KGs, and LLM-KG hybrid approaches.
  arXiv  Detail & Related papers  (2024-07-09T05:42:53Z)
• Knowledge Graph-Enhanced Large Language Models via Path Selection [58.228392005755026]
  Large Language Models (LLMs) have shown unprecedented performance in various real-world applications. LLMs are known to generate factually inaccurate outputs, a.k.a. the hallucination problem. We propose a principled framework KELP with three stages to handle the above problems.
  arXiv  Detail & Related papers  (2024-06-19T21:45:20Z)
• Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering [87.67177556994525]
  We propose a training-free method called Generate-on-Graph (GoG) to generate new factual triples while exploring Knowledge Graphs (KGs) GoG performs reasoning through a Thinking-Searching-Generating framework, which treats LLM as both Agent and KG in IKGQA.
  arXiv  Detail & Related papers  (2024-04-23T04:47:22Z)
• Knowledge Graph Large Language Model (KG-LLM) for Link Prediction [43.55117421485917]
  We introduce the Knowledge Graph Large Language Model (KG-LLM), a novel framework that leverages large language models (LLMs) for knowledge graph tasks. We first convert structured knowledge graph data into natural language and then use these natural language prompts to fine-tune LLMs. To show the efficacy of the KG-LLM Framework, we fine-tune three leading LLMs within this framework, including Flan-T5, LLaMa2 and Gemma.
  arXiv  Detail & Related papers  (2024-03-12T04:47:29Z)
• Large Language Models Can Better Understand Knowledge Graphs Than We Thought [13.336418752729987]
  knowledge graph (KG) embeddings with model parameters become increasingly costly. Current prompting methods often rely on a trial-and-error approach. We show that unordered linearized triples are more effective for LLMs' understanding of KGs compared to fluent NL text.
  arXiv  Detail & Related papers  (2024-02-18T10:44:03Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering [16.434098552925427]
  We study the KG-augmented language model approach for solving the knowledge graph question answering (KGQA) task. We propose an answer-sensitive KG-to-Text approach that can transform KG knowledge into well-textualized statements.
  arXiv  Detail & Related papers  (2023-09-20T10:42:08Z)
• Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling [34.59678835272862]
  ChatGPT, a representative large language model (LLM), has gained considerable attention due to its powerful emergent abilities. This paper proposes to enhance LLMs with knowledge graph-enhanced large language models (KGLLMs) KGLLM provides a solution to enhance LLMs' factual reasoning ability, opening up new avenues for LLM research.
  arXiv  Detail & Related papers  (2023-06-20T12:21:06Z)
• Empowering Language Models with Knowledge Graph Reasoning for Question Answering [117.79170629640525]
  We propose knOwledge REasOning empowered Language Model (OREO-LM) OREO-LM consists of a novel Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs. We show significant performance gain, achieving state-of-art results in the Closed-Book setting.
  arXiv  Detail & Related papers  (2022-11-15T18:26:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.