Facts Do Care About Your Language: Assessing Answer Quality of Multilingual LLMs
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913880639275008 |
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| author | Kansal, Yuval Berman, Shmuel Liu, Lydia |
| author_facet | Kansal, Yuval Berman, Shmuel Liu, Lydia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Facts Do Care About Your Language: Assessing Answer Quality of Multilingual LLMs Kansal, Yuval Berman, Shmuel Liu, Lydia Computation and Language Artificial Intelligence 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. |
| title | Facts Do Care About Your Language: Assessing Answer Quality of Multilingual LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2506.03051 |