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Facts Do Care About Your Language: Assessing Answer Quality of Multilingual LLMs

Published: June 3, 2025 | arXiv ID: 2506.03051v1

By: Yuval Kansal, Shmuel Berman, Lydia Liu

BigTech Affiliations: Princeton University

Potential Business Impact:

Makes learning tools more truthful for all languages.

Business Areas:
Language Learning Education

Factuality is a necessary precursor to useful educational tools. As adoption of Large Language Models (LLMs) in education continues of grow, ensuring correctness in all settings is paramount. Despite their strong English capabilities, LLM performance in other languages is largely untested. In this work, we evaluate the correctness of the Llama3.1 family of models in answering factual questions appropriate for middle and high school students. We demonstrate that LLMs not only provide extraneous and less truthful information, but also exacerbate existing biases against rare languages.

Country of Origin
🇺🇸 United States

Page Count
7 pages

Category
Computer Science:
Computation and Language