https://arxiv.org/ftp/arxiv/papers/2306/2306.07377.pdf
Title: Lost in Translation: Large Language Models in Non-English Content Analysis
Authors: Gabriel Nicholas and Aliya Bhatia
Word Count: Approximately 4,000
Estimated Read Time: 14-16 minutes
Summary:
The paper discusses the potential limitations of using large language models (LLMs), specifically multilingual language models, for analyzing non-English language content online. It provides an overview of how LLMs work, especially multilingual models that are trained on data from multiple languages. The authors note that LLMs tend to be trained mostly on English text and perform inconsistently across languages. They identify several challenges with using LLMs for non-English content analysis:
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Reliance on machine-translated text, which introduces errors
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Problems are difficult to identify and fix due to unintuitive cross-language connections
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Performance varies widely across languages
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Failure to account for local language contexts
The paper provides recommendations for companies, researchers, and governments on improving the use of multilingual LLMs. This includes making models more transparent, deploying them with caution, and investing in building capacity in low-resource languages.
Overall, the paper argues that while multilingual LLMs show promise, their current limitations pose risks, especially when deployed for high-stakes tasks like content moderation. More research and improved data is needed to enable equitable use of LLMs across languages.
For applications development, the paper suggests that multilingual LLMs should be used with caution for content analysis tasks. Due to the limitations noted, they are unlikely to be effective as stand-alone models for complex tasks like sentiment analysis or hate speech detection. Domain-specific training and human oversight would likely be needed. However, the more general representations learned by LLMs could potentially be incorporated into hybrid models for specific domains and languages.
