Multilingual LLM Evaluation in Practical Settings

Thursday, 6th February 2025, 15:00 CET

 

Sebastian Ruder (Meta)

ABSTRACT

Large language models (LLMs) are increasingly used in a variety of applications across the globe but do not provide equal utility across languages. In this talk, I will discuss multilingual evaluation of LLMs in two practical settings: conversational instruction-following and usage of quantized models. For the first part, I will focus on a specific aspect of multilingual conversational ability where errors result in a jarring user experience: generating text in the user’s desired language. I will describe a new benchmark and evaluation of a range of LLMs. We find that even the strongest models exhibit language confusion, i.e., they fail to consistently respond in the correct language. I will discuss what affects language confusion, how to mitigate it, and potential extensions. In the second part, I will discuss the first evaluation study of quantized multilingual LLMs across languages. We find that automatic metrics severely underestimate the negative impact of quantization and that human evaluation—which has been neglected by prior studies—is key to revealing harmful effects. Overall, I highlight limitations of multilingual LLMs and challenges of real-world multilingual evaluation.

LECTURER SHORT CV

Sebastian Ruder is a research scientist at Meta based in Berlin, Germany where he works on improving evaluation and benchmarking of large language models (LLMs). He previously led the Multilinguality team at Cohere with the objective to improve the multilingual capabilities of Cohere’s LLMs. Before that he was a research scientist at Google DeepMind. He completed his PhD in Natural Language Processing (NLP) at the Insight Research Centre for Data Analytics, while working as a research scientist at Dublin-based text analytics startup AYLIEN. Previously, he studied Computational Linguistics at the University of Heidelberg, Germany and at Trinity College, Dublin.

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