mlsanityData to 14 Oct 2026

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

arXiv 2506.03051 ↗First posted 3 Jun 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 3 Jun 2025.

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References · 6

  1. 2023Document-Level Machine Translation with Large Language Modelscited by 90
  2. 2023Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkerscited by 87
  3. 2023Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queriescited by 82
  4. 2024On the Diversity of Synthetic Data and its Impact on Training Large Language Modelscited by 59
  5. 2023LLM-powered Data Augmentation for Enhanced Cross-lingual Performancecited by 51
  6. 2024Evaluating and Mitigating Linguistic Discrimination in Large Language Modelscited by 8

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