---
license: mit
datasets:
- charlieoneill/csLG
- JSALT2024-Astro-LLMs/astro_paper_corpus
language:
- en
tags:
- sparse-autoencoder
- embeddings
- interpretability
- scientific-nlp
---

# Sparse Autoencoders for Scientific Paper Embeddings

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.

## Model Description

### Overview

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.

### Model Architecture

Each SAE follows a top-k architecture with varying hyperparameters:
- k: number of active latents (16, 32, 64, or 128)
- n: total number of latents (3072, 4608, 6144, 9216, or 12288)

The naming convention for the models is:
`{domain}_{k}_{n}_{batch_size}.pth`

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.

## Intended Uses & Limitations

These SAEs are primarily intended for:
1. Extracting interpretable features from dense embeddings of scientific texts
2. Enabling fine-grained control over semantic search in scientific literature
3. Studying the structure of semantic spaces in specific scientific domains

Limitations:
- The models are domain-specific (cs.LG and astro.PH) and may not generalize well to other domains
- Performance may vary depending on the quality and domain-specificity of the input embeddings

## Training Data

The SAEs were trained on embeddings of abstracts from:
- cs.LG: 153,000 papers
- astro.PH: 272,000 papers

## Training Procedure

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).

## Evaluation Results

Performance metrics for various configurations:

|k   |n     |Domain   |MSE    |Log FD  |Act Mean |
|-----|-------|----------|--------|---------|----------|
| 16  | 3072  | astro.PH | 0.2264 | -2.7204 | 0.1264   |
| 16  | 3072  | cs.LG    | 0.2284 | -2.7314 | 0.1332   |
| 64  | 9216  | astro.PH | 0.1182 | -2.4682 | 0.0539   |
| 64  | 9216  | cs.LG    | 0.1240 | -2.3536 | 0.0545   |
| 128 | 12288 | astro.PH | 0.0936 | -2.7025 | 0.0399   |
| 128 | 12288 | cs.LG    | 0.0942 | -2.0858 | 0.0342   |

* __MSE__: Normalised Mean Squared Error
* __Log FD__: Mean log density of feature activations
* __Act Mean__: Mean activation value across non-zero features

For full results, please refer to our paper (link to be added upon publication).

## Ethical Considerations

While these models are designed to improve interpretability, users should be aware that:
1. The extracted features may reflect biases present in the scientific literature used for training
2. Interpretations of the features should be validated carefully, especially when used for decision-making processes

## Citation

If you use these models in your research, please cite our paper (citation to be added upon publication).

## Additional Information

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).