Summary of Do Large Language Models Have An English Accent? Evaluating and Improving the Naturalness Of Multilingual Llms, by Yanzhu Guo et al.
Do Large Language Models Have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs
by Yanzhu Guo, Simone Conia, Zelin Zhou, Min Li, Saloni Potdar, Henry Xiao
First submitted to arxiv on: 21 Oct 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 introduces novel automatic corpus-level metrics to assess the lexical and syntactic naturalness of Large Language Models (LLMs) in a multilingual context. The authors evaluate state-of-the-art LLMs on a curated benchmark in French and Chinese, revealing English-influenced patterns. To mitigate this issue, they propose a simple and effective alignment method that improves naturalness without compromising performance on general-purpose benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn new languages by making sure their language models don’t sound like they were written by an English speaker trying to speak French or Chinese. The authors create special tests to check if language models are speaking naturally in different languages, and they show that most models are not doing a great job. They also offer a way to fix this problem, which helps the models sound more natural while still being good at general tasks. |
Keywords
» Artificial intelligence » Alignment
