Summary of Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models, by Samuel Marks et al.
Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models
by Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, Aaron Mueller
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 paper presents methods for identifying and applying sparse feature circuits in language models. These circuits are causally implicated subnetworks that provide interpretable features, unlike previous work which focused on polysemantic units like attention heads or neurons. The proposed sparse feature circuits enable detailed understanding of unanticipated mechanisms and can be applied to various downstream tasks such as classifier generalization using the SHIFT method. Additionally, the paper introduces an unsupervised and scalable interpretability pipeline that discovers thousands of sparse feature circuits for model behaviors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how language models work by breaking them down into smaller parts called “sparse feature circuits”. These circuits are made up of simpler features that we can understand, unlike previous methods which used complex features like attention heads. By using these circuits, we can improve the performance of language models and make their behaviors more predictable. The paper also shows us how to automatically discover thousands of these circuits without any human supervision. |
Keywords
* Artificial intelligence * Attention * Generalization * Unsupervised
