Summary of Automatically Identifying Local and Global Circuits with Linear Computation Graphs, by Xuyang Ge et al.
Automatically Identifying Local and Global Circuits with Linear Computation Graphs
by Xuyang Ge, Fukang Zhu, Wentao Shu, Junxuan Wang, Zhengfu He, Xipeng Qiu
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A novel approach to mechanistic interpretability is proposed by introducing a circuit discovery pipeline with Sparse Autoencoders (SAEs) and Transcoders. By inserting these modules into the model, the computation graph becomes strictly linear, enabling the identification of end-to-end and local circuits accounting for logits or intermediate features without requiring linear approximation. The pipeline can be scaled using Hierarchical Attribution. This method is applied to GPT-2 Small, revealing new insights into existing discoveries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a way to understand how models work by analyzing their “circuits.” Imagine your brain as a circuit board with different paths that help you think and learn. The team developed a new tool called Sparse Autoencoders (SAEs) and Transcoders, which helps them find these circuits. This allows them to see what parts of the model are important for certain tasks or decisions. They tested this tool on a specific model and found some surprising results that can help us better understand how models think. |
Keywords
» Artificial intelligence » Gpt » Logits
