SciText2Eq: Assessing LLMs for Explainable Equation Generation for Scientific Creativity
This work investigates the ability of large language models (LLMs) to generate mathematical equations from scientific texts. Prior work faces challenges in unstructured grounding, multi-equation dependency, and humanaligned evaluation. To this end, we construct a dataset of AI research papers, pairing contextual passages with ground-truth equations and variable descriptions. We develop an explainable equation generation workflow and evaluate it across diverse open- and closed-source LLM backbone
Record details
Published: 14 June 2026
Source: arXiv
Category: Research
Topics: Transparency · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (14 June 2026), “SciText2Eq: Assessing LLMs for Explainable Equation Generation for Scientific Creativity,” evidence record 987, https://ethics.ai/record/987 (originally published by arXiv).
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