Papers by Olexandr Isayev
MolErr2Fix: Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Correction (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown growing potential in molecular sciences, but they often produce chemically inaccurate descriptions and struggle to recognize or justify potential errors. |
| Approach: | They propose a benchmark to assess LLMs on error detection and correction in molecular descriptions. |
| Outcome: | The proposed benchmark targets LLMs on error detection and correction in molecular descriptions. |