Summary of From Hallucinations to Facts: Enhancing Language Models with Curated Knowledge Graphs, by Ratnesh Kumar Joshi et al.
From Hallucinations to Facts: Enhancing Language Models with Curated Knowledge Graphs
by Ratnesh Kumar Joshi, Sagnik Sengupta, Asif Ekbal
First submitted to arxiv on: 24 Dec 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 The paper addresses the challenge of hallucination in language models by integrating curated knowledge graph (KG) triples to anchor responses in empirical data. It constructs a comprehensive KG repository from Wikipedia, refines it to spotlight essential information for model training, and integrates it with language models to generate linguistically fluent responses grounded in factual accuracy and context relevance. The approach mitigates hallucinations by providing a robust foundation of information, enabling models to draw upon a rich reservoir of factual data during response generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models can sometimes produce answers that are not true or make no sense. This is called “hallucination.” To fix this problem, researchers created a special kind of database called a knowledge graph. They used Wikipedia to build the graph and then made it even better by focusing on the most important information for teaching language models. By giving language models access to this knowledge graph, they can generate answers that are both correct and make sense. This makes the language models more reliable and trustworthy. |
Keywords
» Artificial intelligence » Hallucination » Knowledge graph
