Summary of Evaluating and Mitigating Linguistic Discrimination in Large Language Models, by Guoliang Dong et al.
Evaluating and Mitigating Linguistic Discrimination in Large Language Models
by Guoliang Dong, Haoyu Wang, Jun Sun, Xinyu Wang
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Software Engineering (cs.SE)
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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 In this study, researchers investigate the multilingual capabilities and potential biases of large language models (LLMs). They find that while LLMs excel at solving tasks across various languages, they can also exhibit linguistic discrimination due to uneven training data distribution. This means that when faced with the same task in different languages, LLMs may struggle to maintain consistency in their responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how large language models can be both very good and very bad at understanding text from different languages. On one hand, they’re great at solving problems described in many languages. But on the other hand, they might not always give the same answers when shown the same task written in a different language. |
