Summary of Transcoders Find Interpretable Llm Feature Circuits, by Jacob Dunefsky and Philippe Chlenski and Neel Nanda
Transcoders Find Interpretable LLM Feature Circuits
by Jacob Dunefsky, Philippe Chlenski, Neel Nanda
First submitted to arxiv on: 17 Jun 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 The paper introduces a novel approach to mechanistic interpretability, specifically circuit analysis for transformer-based language models. The method uses transcoders to approximate densely activating MLP layers with wider, sparsely activating layers, enabling weights-based circuit analysis. This leads to factorized circuits that separate input-dependent and input-invariant terms. The transcoders are trained on large-scale language models and demonstrate comparable performance to sparse autoencoders in terms of sparsity, faithfulness, and human interpretability. The approach is applied to reverse-engineer unknown circuits in a model, providing novel insights into the “greater-than circuit” in GPT2-small. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how machines learn by looking at the inner workings of language models. It’s like trying to figure out what makes your favorite video game work, but instead of code, it’s about understanding how words and sentences are processed. The researchers created a new way to do this using something called transcoders, which can simplify complex computations into smaller, more understandable pieces. This is important because it allows us to see what the model is really doing when it makes predictions or generates text. |
Keywords
* Artificial intelligence * Transformer
