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In recent years, large language models (e.g., Open AI's GPT-4, Meta's LLaMa,Google's PaLM) have become the dominant approach for building AI systems toanalyze and generate language online. However, the automated systems thatincreasingly mediate our interactions online -- such as chatbots, contentmoderation systems, and search engines -- are primarily designed for and workfar more effectively in English than in the world's other 7,000 languages.Recently, researchers and technology companies have attempted to extend thecapabilities of large language models into languages other than English bybuilding what are called multilingual language models.  In this paper, we explain how these multilingual language models work andexplore their capabilities and limits. Part I provides a simple technicalexplanation of how large language models work, why there is a gap in availabledata between English and other languages, and how multilingual language modelsattempt to bridge that gap. Part II accounts for the challenges of doingcontent analysis with large language models in general and multilinguallanguage models in particular. Part III offers recommendations for companies,researchers, and policymakers to keep in mind when considering researching,developing and deploying large and multilingual language models.

Lost in Translation: Large Language Models in Non-English Content Analysis