Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) based applications including automated text generation, question answering, chatbots, and others. However, they face a significant challenge: hallucinations, where models produce plausible-sounding but factually incor...
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| Language: | English |
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Elsevier
2025-05-01
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| Series: | Web Semantics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1570826824000301 |
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| author | Ernests Lavrinovics Russa Biswas Johannes Bjerva Katja Hose |
| author_facet | Ernests Lavrinovics Russa Biswas Johannes Bjerva Katja Hose |
| author_sort | Ernests Lavrinovics |
| collection | DOAJ |
| description | Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) based applications including automated text generation, question answering, chatbots, and others. However, they face a significant challenge: hallucinations, where models produce plausible-sounding but factually incorrect responses. This undermines trust and limits the applicability of LLMs in different domains. Knowledge Graphs (KGs), on the other hand, provide a structured collection of interconnected facts represented as entities (nodes) and their relationships (edges). In recent research, KGs have been leveraged to provide context that can fill gaps in an LLM’s understanding of certain topics offering a promising approach to mitigate hallucinations in LLMs, enhancing their reliability and accuracy while benefiting from their wide applicability. Nonetheless, it is still a very active area of research with various unresolved open problems. In this paper, we discuss these open challenges covering state-of-the-art datasets and benchmarks as well as methods for knowledge integration and evaluating hallucinations. In our discussion, we consider the current use of KGs in LLM systems and identify future directions within each of these challenges. |
| format | Article |
| id | doaj-art-ba2f79c6e7c04e868622af96548d799d |
| institution | DOAJ |
| issn | 1570-8268 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Web Semantics |
| spelling | doaj-art-ba2f79c6e7c04e868622af96548d799d2025-08-20T02:52:23ZengElsevierWeb Semantics1570-82682025-05-018510084410.1016/j.websem.2024.100844Knowledge Graphs, Large Language Models, and Hallucinations: An NLP PerspectiveErnests Lavrinovics0Russa Biswas1Johannes Bjerva2Katja Hose3Department of Computer Science, Aalborg University, Copenhagen, Denmark; Corresponding author.Department of Computer Science, Aalborg University, Copenhagen, DenmarkDepartment of Computer Science, Aalborg University, Copenhagen, DenmarkInstitute of Logic and Computation, TU Wien, Vienna, AustriaLarge Language Models (LLMs) have revolutionized Natural Language Processing (NLP) based applications including automated text generation, question answering, chatbots, and others. However, they face a significant challenge: hallucinations, where models produce plausible-sounding but factually incorrect responses. This undermines trust and limits the applicability of LLMs in different domains. Knowledge Graphs (KGs), on the other hand, provide a structured collection of interconnected facts represented as entities (nodes) and their relationships (edges). In recent research, KGs have been leveraged to provide context that can fill gaps in an LLM’s understanding of certain topics offering a promising approach to mitigate hallucinations in LLMs, enhancing their reliability and accuracy while benefiting from their wide applicability. Nonetheless, it is still a very active area of research with various unresolved open problems. In this paper, we discuss these open challenges covering state-of-the-art datasets and benchmarks as well as methods for knowledge integration and evaluating hallucinations. In our discussion, we consider the current use of KGs in LLM systems and identify future directions within each of these challenges.http://www.sciencedirect.com/science/article/pii/S1570826824000301LLMFactualityKnowledge GraphsHallucinations |
| spellingShingle | Ernests Lavrinovics Russa Biswas Johannes Bjerva Katja Hose Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective Web Semantics LLM Factuality Knowledge Graphs Hallucinations |
| title | Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective |
| title_full | Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective |
| title_fullStr | Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective |
| title_full_unstemmed | Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective |
| title_short | Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective |
| title_sort | knowledge graphs large language models and hallucinations an nlp perspective |
| topic | LLM Factuality Knowledge Graphs Hallucinations |
| url | http://www.sciencedirect.com/science/article/pii/S1570826824000301 |
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