Explaining Attention with Program Synthesis
📰 ArXiv cs.AI
Learn to explain attention mechanisms in deep learning using program synthesis, enabling more interpretable models
Action Steps
- Compute attention matrices for a given attention head using a collection of random inputs
- Use program synthesis to approximate the behavior of the attention head with an executable program
- Analyze the synthesized program to understand the attention mechanism
- Apply the insights gained to improve the interpretability of deep learning models
- Test the approach on various transformer language models to evaluate its effectiveness
Who Needs to Know This
Researchers and engineers working on interpretable deep learning and natural language processing can benefit from this approach to understand attention mechanisms better
Key Insight
💡 Program synthesis can be used to approximate the behavior of attention heads in transformer language models, providing a more interpretable representation of the model's computations
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🤖 Explain attention mechanisms in deep learning using program synthesis! 📊
Full Article
Title: Explaining Attention with Program Synthesis
Abstract:
arXiv:2606.19317v2 Announce Type: replace-cross Abstract: A longstanding goal of research on interpretable deep learning is to replace opaque neural computations with human-meaningful symbolic descriptions. In this paper, we propose an approach for approximating the behavior of components of deep networks with executable programs. We focus on attention heads in transformer language models. For a given head, we first compute its associated attention matrices on a collection of randomly selected t
Abstract:
arXiv:2606.19317v2 Announce Type: replace-cross Abstract: A longstanding goal of research on interpretable deep learning is to replace opaque neural computations with human-meaningful symbolic descriptions. In this paper, we propose an approach for approximating the behavior of components of deep networks with executable programs. We focus on attention heads in transformer language models. For a given head, we first compute its associated attention matrices on a collection of randomly selected t
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