Summary of Tackling Polysemanticity with Neuron Embeddings, by Alex Foote
Tackling Polysemanticity with Neuron Embeddings
by Alex Foote
First submitted to arxiv on: 12 Nov 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 The paper proposes “neuron embeddings” as a means to address polysemanticity in neural networks by analyzing the internal representations and weights of models. This approach is demonstrated on GPT2-small, with a user-friendly interface for exploring the results. The neuron embeddings are computed using a model’s internal representations and weights, making them architecture-agnostic and eliminating the risk of introducing external structure that may not reflect a model’s actual computation. Additionally, the paper discusses how neuron embeddings can be used to measure polysemanticity, which could inform more effective evaluation of models like Sparse Auto-Encoders (SAEs). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how our brains work by creating a special kind of map for each part of a neural network. This map shows what each part is good at and how it’s connected to other parts. By looking at these maps, we can see if different parts of the brain are doing similar things or if they’re specialized in their own way. The paper uses this idea to create a tool that helps us understand how well neural networks work together. This could be helpful for making better artificial intelligence models. |
Keywords
» Artificial intelligence » Neural network
