LLMs' molecular prediction abilities tested for memorization vs. true learning

A new study published on arXiv investigates the in-context learning capabilities of large language models (LLMs) for molecular property prediction. Researchers explored whether models like GPT-4.1, GPT-5, and Gemini 2.5 genuinely perform regression or rely on memorized data. Through a series of blinded experiments on MoleculeNet datasets, the study found no evidence of verbatim retrieval and highlighted conflicts between pre-trained knowledge and in-context information when data access is progressively limited. AI

IMPACT This research provides a framework for understanding LLM performance on scientific tasks, potentially guiding future model development and evaluation for specialized domains.

RANK_REASON The cluster contains an academic paper detailing research into LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Delaney solubility
• Gemini 2.5
• GPT-4.1
• GPT-5
• lipophilicity
• Matthias Busch
• MoleculeNet: a benchmark for molecular machine learning.
• QM7 atomization energy

• paper
• model release

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