Summary of Knowledge Graphs, Large Language Models, and Hallucinations: An Nlp Perspective, by Ernests Lavrinovics et al.
Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective
by Ernests Lavrinovics, Russa Biswas, Johannes Bjerva, Katja Hose
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent breakthrough in Natural Language Processing (NLP) has led to the development of Large Language Models (LLMs) capable of generating text, answering questions, and powering chatbots. However, these models are not without limitations, as they often produce plausible but factually incorrect responses, known as hallucinations. This challenge undermines trust and restricts their applicability in various domains. To address this issue, researchers have turned to Knowledge Graphs (KGs), which provide a structured collection of interconnected facts. By integrating KGs with LLMs, it is possible to enhance their reliability and accuracy while leveraging the wide applicability of LLMs. Despite progress, there are still unresolved open problems in this area, including state-of-the-art datasets and benchmarks, methods for knowledge integration, and evaluating hallucinations. This paper discusses these challenges and future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can do many things like write text or answer questions, but sometimes they make mistakes. These mistakes are called hallucinations. They sound believable but aren’t true. This makes it hard to trust the models. One way to fix this is by using Knowledge Graphs, which are collections of facts connected together. By combining these graphs with the language models, we can make them more reliable and accurate. There’s still work to be done, but researchers are making progress. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
