Summary of Can We Catch the Elephant? a Survey Of the Evolvement Of Hallucination Evaluation on Natural Language Generation, by Siya Qi et al.
Can We Catch the Elephant? A Survey of the Evolvement of Hallucination Evaluation on Natural Language Generation
by Siya Qi, Yulan He, Zheng Yuan
First submitted to arxiv on: 18 Apr 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 This paper explores the phenomenon of hallucination in Natural Language Generation (NLG), where text generation models produce factual information that does not exist in the original input. The authors highlight the importance of evaluating this aspect of NLG, as recent advancements have led to improved fluency and grammaticality of generated text. The survey presents a comprehensive overview of existing evaluation methods for hallucination, categorized along three dimensions: fact granularity, evaluator design principles, and assessment facets. By examining these diverse approaches, researchers can identify limitations and potential future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if computers could generate written text that sounds like it was written by a human. Sounds cool, right? But sometimes these machines make mistakes and create fake information that didn’t exist in the first place. This is called “hallucination” in language generation. Researchers are trying to figure out how to evaluate this mistake-making process so they can improve the quality of generated text. In this study, experts surveyed various methods for evaluating hallucination and identified areas where more work needs to be done. |
Keywords
» Artificial intelligence » Hallucination » Text generation
