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| title: PLMNeuron |
| emoji: 🧠 |
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| **PLMNeuron** is an interdisciplinary research collective at UC Berkeley dedicated to uncovering how protein language models “think.” |
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| 🔬 We investigate the inner workings of state-of-the-art protein language models (PLMs), such as ESM2, by mapping biophysical and functional properties—like charge, hydrophobicity, and secondary structure motifs—onto individual neurons. Our goal is to turn black-box embeddings into transparent, interpretable, and steerable representations. |
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| 📚 This organization hosts datasets and resources for the paper: |
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| **"Automated Neuron Labelling Enables Generative Steering and Interpretability in Protein Language Models"** |
| *Proceedings of the Workshop on Generative AI for Biology at the 42nd International Conference on Machine Learning (ICML), Vancouver, Canada. PMLR 267, 2025.* |
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| [](https://huggingface.co/papers/2507.06458) |
| [](https://arxiv.org/abs/2507.06458) |
| [](https://icml.cc/virtual/2025/51259) |
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| 🧬 These datasets enable research on interpretability and generative control by providing natural language annotations of individual neurons across multiple layers and model sizes. |
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| ➡️ We welcome collaboration! Dive into the datasets or reach out to connect. |
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| ### 📊 Dataset Index (Work in Progress) |
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| | Dataset Name | Link | Description | Schema Overview | |
| |--------------|------|-------------|------------------| |
| | `protolyze/esm3B_500k_neuron_explanations_redo` | [🔗 Link](https://huggingface.co/datasets/protolyze/esm3B_500k_neuron_explanations_redo) | Annotations of neurons in ESM2-t36-3B with interpretable biophysical and functional features. | `neuron_id`, `explanation_1`, `explanation_2` | |
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