Date
Publisher
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.
Study at a glance
- Question: How accurately do AI models answer middle and high school factual questions across different languages?
- Setting and data: Factual questions appropriate for middle and high school students
- Method: Tests how accurately AI models answer factual questions across languages
Why it matters
Factuality is a necessary precursor to useful educational tools. While adoption of large language models in education continues to grow, their performance in languages other than English is largely untested.
Key findings
- For middle and high school factual questions, language models provide less truthful information.
- Language models provide extraneous information when answering factual questions.
- Language models worsen existing biases against rare languages.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
