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Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains

arXiv — cs.CL•Monday, November 3, 2025 at 5:00:00 AM
NeutralArtificial Intelligence

Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains

The emergence of Large Language Models (LLMs) has transformed the landscape of Machine Translation (MT), allowing for more nuanced and fluent translations across various languages. However, recent studies indicate that these models do not perform uniformly across different language families and specialized domains. Additionally, they may inadvertently perpetuate biases found in their training data, raising concerns about fairness and representation in AI. Understanding these limitations is crucial as we continue to rely on LLMs for communication and information sharing.
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