neural-network-interpretability

This paper studies seed dependence in sparse autoencoders, finding that stable features carry most predictive signal while unstable features reflect reproducible low-dimensional subspaces.

This paper introduces Attention-Shifting (AS), a novel framework for selective machine unlearning in LLMs that balances effective removal of sensitive information while preventing hallucinations and preserving model utility. The method uses importance-aware attention suppression and retention enhancement to achieve up to 15% higher accuracy preservation compared to existing unlearning approaches on standard benchmarks.

OpenAI introduces Activation Atlases, a technique for visualizing and understanding the internal representations of neural networks, enabling humans to discover spurious correlations and unexpected behaviors such as fooling image classifiers by adding noodles to images.