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
- Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Data [0.9284740716447338]
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation.
Recent research has shown promising results in leveraging knowledge graphs (KGs) to enhance LLM performance.
We have developed different techniques that tightly integrate KG structures and semantics into LLM representations.
arXiv Detail & Related papers (2024-12-14T02:51:47Z) - KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning [74.21524111840652]
This paper proposes textbfKaLM, a textitKnowledge-aligned Language Modeling approach.
It fine-tunes autoregressive large language models to align with KG knowledge via the joint objective of explicit knowledge alignment and implicit knowledge alignment.
Notably, our method achieves a significant performance boost in evaluations of knowledge-driven tasks.
arXiv Detail & Related papers (2024-12-06T11:08:24Z) - 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) - Large Language Models Can Better Understand Knowledge Graphs Than We Thought [13.336418752729987]
We study how large language models (LLMs) process and interpret knowledge graphs (KGs)
At the literal level, we reveal LLMs' preferences for various input formats.
At the attention distribution level, we discuss the underlying mechanisms driving these preferences.
arXiv Detail & Related papers (2024-02-18T10:44:03Z) - 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.