Summary of Investigating Neuron Ablation in Attention Heads: the Case For Peak Activation Centering, by Nicholas Pochinkov et al.
Investigating Neuron Ablation in Attention Heads: The Case for Peak Activation Centering
by Nicholas Pochinkov, Ben Pasero, Skylar Shibayama
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 research paper investigates the attention mechanisms in transformer-based models, a rapidly growing area of study. The focus is on understanding how neuron activations represent concepts and developing methods to interpret these models. Specifically, the authors explore four neural ablation techniques: zero ablation, mean ablation, activation resampling, and peak ablation. They compare the effectiveness of each method in language models and vision transformers using various evaluation metrics. The results show that each technique can be optimal for different regimes and models, with resampling generally causing the most significant performance deterioration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformer-based models are becoming increasingly popular across many fields. This study aims to improve our understanding of how these models work, particularly when it comes to attention mechanisms. To do this, researchers look at neuron activations in different ways and test four techniques: zero ablation, mean ablation, activation resampling, and peak ablation. They find that each method can be useful in different situations, and some cause more harm than others. |
Keywords
» Artificial intelligence » Attention » Transformer
