Summary of Comparing Hallucination Detection Metrics For Multilingual Generation, by Haoqiang Kang et al.
Comparing Hallucination Detection Metrics for Multilingual Generation
by Haoqiang Kang, Terra Blevins, Luke Zettlemoyer
First submitted to arxiv on: 16 Feb 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 investigates the effectiveness of various metrics in detecting factual hallucinations in generated biographical summaries across different languages. Specifically, it compares the performance of lexical metrics like ROUGE and Named Entity Overlap with Natural Language Inference (NLI)-based metrics in identifying hallucinations. The analysis reveals that while lexical metrics are ineffective, NLI-based metrics perform well, correlating with human annotations in many settings and often outperforming supervised models. However, these metrics have limitations, including failing to detect single-fact hallucinations well and struggling with lower-resource languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how good different methods are at finding mistakes in fake biographical summaries when they’re written in different languages. It compares two kinds of methods: ones that use words and phrases (like ROUGE) and ones that understand the meaning of sentences (called Natural Language Inference or NLI). The results show that the word-based methods don’t work well, but the sentence-understanding methods do a good job, especially when they’re compared to what humans think. However, even the best method isn’t perfect – it doesn’t catch small mistakes and struggles with languages where there’s less information available. |
Keywords
» Artificial intelligence » Inference » Rouge » Supervised
