Summary of Large Language Models Are Easily Confused: a Quantitative Metric, Security Implications and Typological Analysis, by Yiyi Chen et al.
Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis
by Yiyi Chen, Qiongxiu Li, Russa Biswas, Johannes Bjerva
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 explores Language Confusion, a phenomenon where Large Language Models (LLMs) generate text in unexpected languages or contexts. Researchers introduce the Language Confusion Entropy metric to quantify this confusion, based on linguistic typology and lexical variation. The study compares the new metric with the Language Confusion Benchmark and finds patterns of language confusion across LLMs. Additionally, it links language confusion to LLM security, revealing potential vulnerabilities in multilingual embedding inversion attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language Confusion is a problem where Large Language Models can’t always speak the same language as humans. This makes it hard for them to understand what we want them to say. Scientists looked at why this happens and developed a new way to measure how confused LLMs get. They found that different models are more or less confused, but there are patterns they can use to improve language understanding. They also discovered that this confusion could be used by hackers to attack these models. |
Keywords
» Artificial intelligence » Embedding » Language understanding
