Summary of Disentangling Dense Embeddings with Sparse Autoencoders, by Charles O’neill et al.
Disentangling Dense Embeddings with Sparse Autoencoders
by Charles O’Neill, Christine Ye, Kartheik Iyer, John F. Wu
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research explores the application of sparse autoencoders (SAEs) to dense text embeddings from large language models, demonstrating their effectiveness in disentangling semantic concepts. By training SAEs on over 420,000 scientific paper abstracts from computer science and astronomy, the study shows that the resulting sparse representations maintain semantic fidelity while offering interpretability. The learned features are analyzed, revealing their behavior across different model capacities and introducing a novel method for identifying “feature families” representing related concepts at varying levels of abstraction. This approach enables precise steering of semantic search, allowing for fine-grained control over query semantics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special machines called autoencoders to help understand the meaning behind big language models’ ideas. They take lots of text from scientific papers and make it easier to see what’s important. By doing this, they can make computers better at searching through texts based on what we want them to find. The researchers also found a way to group related concepts together, making it simpler to understand how these big language models work. |
Keywords
» Artificial intelligence » Semantics
