Summary of Faux Polyglot: a Study on Information Disparity in Multilingual Large Language Models, by Nikhil Sharma et al.
Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models
by Nikhil Sharma, Kenton Murray, Ziang Xiao
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 linguistic preferences of Large Language Models (LLMs) in cross-language settings, where multiple languages are present. The study found that LLMs exhibit a systemic bias towards information in the same language as the query language for both document retrieval and answer generation. This bias is evident even when no relevant information exists in the query language, with LLMs favoring documents from high-resource languages during generation. The results highlight the potential reinforcement of dominant views and further marginalization of low-resource language perspectives. The findings suggest that the multilingual capability of LLMs may have unintended consequences on information parity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re searching for information online, but you don’t speak the same language as most of the results. This paper looks at how large AI models handle this situation. It found that these models tend to prefer information in the same language as your search query. Even when there’s no relevant info in your native language, they’ll often pick documents from languages that are more widely spoken. This can make it harder for people who speak less common languages to find what they’re looking for. The research suggests that this multilingual capability of AI models might actually perpetuate existing language barriers instead of breaking them down. |
