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--- |
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license: mit |
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datasets: |
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- charlieoneill/csLG |
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- JSALT2024-Astro-LLMs/astro_paper_corpus |
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language: |
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- en |
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tags: |
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- sparse-autoencoder |
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- embeddings |
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- interpretability |
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- scientific-nlp |
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--- |
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# Sparse Autoencoders for Scientific Paper Embeddings |
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This repository contains a collection of Sparse Autoencoders (SAEs) trained on embeddings from scientific papers in two domains: Computer Science (cs.LG) and Astrophysics (astro.PH). These SAEs are designed to disentangle semantic concepts in dense embeddings while maintaining semantic fidelity. |
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## Model Description |
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### Overview |
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The SAEs in this repository are trained on embeddings of scientific paper abstracts from arXiv, specifically from the cs.LG (Computer Science - Machine Learning) and astro.PH (Astrophysics) categories. They are designed to extract interpretable features from dense text embeddings derived from large language models. |
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### Model Architecture |
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Each SAE follows a top-k architecture with varying hyperparameters: |
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- k: number of active latents (16, 32, 64, or 128) |
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- n: total number of latents (3072, 4608, 6144, 9216, or 12288) |
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The naming convention for the models is: |
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`{domain}_{k}_{n}_{batch_size}.pth` |
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For example, `csLG_128_3072_256.pth` represents an SAE trained on cs.LG data with k=128, n=3072, and a batch size of 256. |
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## Intended Uses & Limitations |
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These SAEs are primarily intended for: |
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1. Extracting interpretable features from dense embeddings of scientific texts |
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2. Enabling fine-grained control over semantic search in scientific literature |
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3. Studying the structure of semantic spaces in specific scientific domains |
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Limitations: |
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- The models are domain-specific (cs.LG and astro.PH) and may not generalize well to other domains |
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- Performance may vary depending on the quality and domain-specificity of the input embeddings |
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## Training Data |
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The SAEs were trained on embeddings of abstracts from: |
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- cs.LG: 153,000 papers |
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- astro.PH: 272,000 papers |
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## Training Procedure |
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The SAEs were trained using a custom loss function combining reconstruction loss, sparsity constraints, and an auxiliary loss. For detailed training procedures, please refer to our paper (link to be added upon publication). |
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## Evaluation Results |
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Performance metrics for various configurations: |
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|k |n |Domain |MSE |Log FD |Act Mean | |
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|-----|-------|----------|--------|---------|----------| |
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| 16 | 3072 | astro.PH | 0.2264 | -2.7204 | 0.1264 | |
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| 16 | 3072 | cs.LG | 0.2284 | -2.7314 | 0.1332 | |
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| 64 | 9216 | astro.PH | 0.1182 | -2.4682 | 0.0539 | |
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| 64 | 9216 | cs.LG | 0.1240 | -2.3536 | 0.0545 | |
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| 128 | 12288 | astro.PH | 0.0936 | -2.7025 | 0.0399 | |
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| 128 | 12288 | cs.LG | 0.0942 | -2.0858 | 0.0342 | |
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* __MSE__: Normalised Mean Squared Error |
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* __Log FD__: Mean log density of feature activations |
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* __Act Mean__: Mean activation value across non-zero features |
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For full results, please refer to our paper (link to be added upon publication). |
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## Ethical Considerations |
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While these models are designed to improve interpretability, users should be aware that: |
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1. The extracted features may reflect biases present in the scientific literature used for training |
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2. Interpretations of the features should be validated carefully, especially when used for decision-making processes |
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## Citation |
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If you use these models in your research, please cite our paper (citation to be added upon publication). |
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## Additional Information |
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For more details on the methodology, feature families, and applications in semantic search, please refer to our full paper (link to be added upon publication). |
