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Found 2 papers, 1 papers with code
Benchmarking Large Language Models for Molecule Prediction Tasks
Our investigation reveals several key insights: Firstly, LLMs generally lag behind ML models in achieving competitive performance on molecule tasks, particularly when compared to models adept at capturing the geometric structure of molecules, highlighting the constrained ability of LLMs to comprehend graph data.
Harnessing Large Language Models as Post-hoc Correctors
We show that, through our proposed training-free framework LlmCorr, an LLM can work as a post-hoc corrector to propose corrections for the predictions of an arbitrary ML model.
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