How Well Can LLMs Verbalize Mathematical Formulas? A Multi-Domain Evaluation
We evaluated five state-of-the-art LLMs across six scientific domains and constructed Read-Aloud Math, a balanced multi-domain corpus with LaTeX formulas, manually validated simple and extended transcriptions, multiple variants, textual context, domain labels, and formula complexity levels.
ProofPaper ↗
Key points
- Large Language Models (LLMs) have shown strong capabilities across natural language tasks.
- But their ability to generate accurate written verbalizations of mathematical formulas has not been systematically evaluated.
- Such verbalization is important for text-to-speech systems supporting audio-based learning and visually impaired users.
- Overall, LLM-based formula verbalization is promising but remains sensitive to transcription type, textual context, and formula characteristics.
Sources (1)
- [1]How Well Can LLMs Verbalize Mathematical Formulas? A Multi-Domain EvaluationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 11:35 PM
We evaluated five state-of-the-art LLMs across six scientific domains and constructed Read-Aloud Math, a balanced multi-domain corpus with LaTeX formulas, manually validated simple and extended transcriptions, multiple variants, textual context, domain labels, and formula complexity levels.
Large Language Models (LLMs) have shown strong capabilities across natural language tasks.
Extractive summary: sentences quoted from the sources.
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