The paper introduces "InterPLM," a systematic framework for interpreting protein language models (PLMs) using sparse autoencoders (SAEs). This method successfully extracts thousands of interpretable features from PLMs like ESM-2, revealing biological concepts such as binding sites and functional domains that are stored in superposition within the model's neurons. The research demonstrates that SAE...去小宇宙查看完整单集简介
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