Summary of Dictionary Learning Improves Patch-free Circuit Discovery in Mechanistic Interpretability: a Case Study on Othello-gpt, by Zhengfu He et al.
Dictionary Learning Improves Patch-Free Circuit Discovery in Mechanistic Interpretability: A Case Study on Othello-GPT
by Zhengfu He, Xuyang Ge, Qiong Tang, Tianxiang Sun, Qinyuan Cheng, Xipeng Qiu
First submitted to arxiv on: 19 Feb 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 Sparse dictionary learning has been gaining traction for mechanistic interpretability, helping to extract more understandable features from model activations. This paper takes it a step further by asking: how can we identify the underlying circuits connecting these dictionary features? The authors propose an alternative circuit discovery framework that’s less prone to out-of-distribution errors and more efficient in terms of complexity. The framework is based on decomposing dictionary features from various modules, including embedding, attention output, and MLP output. By tracing back to lower-level features, the authors can compute their contributions to more interpretable model behaviors. They demonstrate this approach on a small transformer trained on Othello, revealing human-understandable fine-grained circuits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning models easier to understand. Right now, these models are very good at doing tasks like image recognition and language translation, but it’s hard for humans to know why they’re making certain decisions. The authors of this paper want to change that by finding the underlying patterns or “circuits” in the model’s behavior. They propose a new way to do this that’s more efficient and accurate than previous methods. By breaking down the model’s internal workings, they can identify these circuits and understand how the model is making decisions. This is an important step towards creating models that are not only good at their job but also transparent and trustworthy. |
Keywords
* Artificial intelligence * Attention * Embedding * Machine learning * Transformer * Translation
