Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 238-244 |
| Number of pages | 7 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 273 |
| State | Published - 2025 |
| Event | 39th Annual AAAI Conference on Innovation and Responsibility in AI-Supported Education Workshop, iRAISE 2025 - Philadelphia, United States Duration: Mar 3 2025 → … |
All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Statistics and Probability
- Artificial Intelligence
Keywords
- AI in Education
- Factuality
- LLMs
- Question Answering
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