Summary of The Roles Of English in Evaluating Multilingual Language Models, by Wessel Poelman et al.
The Roles of English in Evaluating Multilingual Language Models
by Wessel Poelman, Miryam de Lhoneux
First submitted to arxiv on: 11 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 This position paper argues that the role of English in multilingual natural language processing (NLP) evaluations is multifaceted, serving both as an interface and a natural language. The authors highlight the difference between these two roles, with task performance being prioritized through English-based prompting and language understanding being the ultimate goal. By analyzing datasets and evaluation setups, the paper reveals that many existing works rely on English as an interface to boost task performance, which may not accurately reflect language understanding. To move forward, the authors recommend a shift from using English as an interface to focusing on improving language understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multilingual natural language processing is becoming more important, with many languages being studied. Right now, English is often used to test how well language models (LMs) work in different languages. But what’s the point of using English? This paper says there are two main reasons: as a way to get language models working better and as a natural part of language understanding. The authors think that these two goals are different, so we should focus on making sure LMs understand language instead of just getting them to work well. |
Keywords
» Artificial intelligence » Language understanding » Natural language processing » Nlp » Prompting
