Summary of Improving Neuron-level Interpretability with White-box Language Models, by Hao Bai et al.
Improving Neuron-level Interpretability with White-box Language Models
by Hao Bai, Yi Ma
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 Recent studies have demonstrated the potential of post-hoc sparse coding techniques, such as dictionary learning, to enhance interpretability in auto-regressive language models like GPT-2. Building on this work, our research focuses on fundamentally improving neural network interpretability by integrating sparse coding into the model architecture. We introduce a novel white-box transformer-like architecture, Coding RAte TransformEr (CRATE), designed to capture low-dimensional structures within data distributions. Our experiments demonstrate significant improvements in neuron-level interpretability across various evaluation metrics, with relative improvements reaching up to 103%. Detailed investigations confirm CRATE’s robust performance and ability to consistently activate on relevant tokens. This research points towards a promising direction for creating white-box foundation models that excel in neuron-level interpretation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to understand how language models like GPT-2 make decisions. Researchers have found ways to “look inside” these models by analyzing the patterns of their neurons. To take this a step further, we’re developing a new kind of model that can do this analysis directly within itself. Our approach is called CRATE, and it’s designed to capture important patterns in data. By using CRATE, we found that language models become much better at explaining their decisions, with improvements reaching up to 103%. This technology has the potential to create more transparent and trustworthy AI systems. |
Keywords
» Artificial intelligence » Gpt » Neural network » Transformer
