arXiv:2601.22594v2 Announce Type: replace Abstract: The high-level concepts that a neural network uses to perform computation need not be aligned to individual neurons (Smolensky, 1986). Language model interpretability research has thus turned to techniques which decompose the neuron basis into more interpretable units of model computation, such as sparse autoencoders (SAEs). However, not all neuron-based representations are uninterpretable. For the first time, we empirically show that MLP neurons are as sparse a feature basis as SAEs. We use this finding to develop an end-to-end gradient-base
The paper represents a significant empirical finding in the ongoing research to understand and optimize large language models, particularly as interpretability and efficiency become critical for practical deployment.
Improved understanding of language model internal workings is crucial for advancing AI capabilities, ensuring reliability, and enabling more efficient and scalable model architectures.
The research challenges a prevailing assumption about the interpretability of standard neuron bases (MLP neurons) compared to more complex sparse autoencoders, potentially simplifying future interpretability efforts.
- · AI researchers
- · Developers of interpretable AI
- · Companies building large language models
- · Developers solely focused on sparse autoencoders for interpretability
This discovery could lead to simpler and more direct methods for interpreting and debugging AI models.
It might influence the architectural design of future language models, favoring more inherently interpretable structures.
Greater interpretability could accelerate the adoption of AI in sensitive applications and increase public trust in AI systems.
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Read at arXiv cs.CL