The field of large language models is witnessing a significant shift towards efficient interpretation and compression techniques. Researchers are exploring innovative methods to reduce computational costs and improve model understanding, including sparse activation filtering, inference-time decomposition of activations, and sparse autoencoder efficiency improvements. These approaches aim to mitigate the challenges posed by the growing size of large language models, such as computational inefficiencies and interpretability issues. Noteworthy papers in this area include:

• COUNTDOWN, which proposes a sparse activation method that can omit 90% of computations with minimal performance loss.
• ITDA, which introduces a scalable approach to interpreting large language models using inference-time decomposition of activations, enabling cross-model comparisons and achieving similar reconstruction performance to sparse autoencoders.
• KronSAE, which factorizes the latent representation via Kronecker product decomposition, reducing memory and computational overhead.
• SAEMA, which validates the stratified structure of representation and demonstrates the impact of changes in representational structure on reconstruction performance.
• Navigating the Latent Space Dynamics of Neural Models, which presents an alternative interpretation of neural models as dynamical systems acting on the latent manifold, enabling the analysis of model properties and data.