arXiv:2606.19317v1 Announce Type: 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 training examples. Next, we prompt a pre-trained language model with a summary of these matrices, and instruc
The increasing complexity and opacity of large language models necessitate new methods for understanding their internal workings, driving research into interpretable AI.
This research provides a novel approach to make deep learning models, particularly Transformer attention mechanisms, more transparent and auditable by approximating their behavior with symbolic programs.
The ability to generate executable programs from neural computations could lead to more reliable, debuggable, and explainable AI systems, accelerating adoption in critical applications.
- · AI safety researchers
- · Developers of auditable AI systems
- · Industries requiring explainable AI
- · Black box AI solutions
- · Systems highly reliant on uninterpretable models
Improved understanding and debugging of large language models and their attention mechanisms.
Acceleration of AI adoption in regulated industries due to enhanced interpretability and trustworthiness.
Potential for automated verification and optimization of AI model logic through symbolic representations.
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Read at arXiv cs.AI