Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation

arXiv:2412.14642v4 Announce Type: replace Abstract: Recently, Large Language Models (LLMs) have demonstrated great potential in natural language-driven molecule discovery. However, existing datasets and benchmarks for molecule-text alignment are predominantly built on one-to-one mappings, measuring LLMs' ability to retrieve a single, pre-defined answer, rather than their creative potential to generate diverse, yet equally valid, molecular candidates. To address this critical gap, we propose Speak-to-Structure (S^2-Bench), the first benchmark to evaluate LLMs in open-domain natural language-dri

Source: arXiv cs.CL — read the full report at the original publisher.

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