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cs.CL2025
Minimal and Mechanistic Conditions for Behavioral Self-Awareness in LLMs
Matthew Bozoukov, Matthew Nguyen, Shubkarman Singh +2
Recent studies have revealed that LLMs can exhibit behavioral self-awareness: the ability to accurately describe or predict their own learned behaviors without explicit supervision…
cs.LG2025
Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models
Patrick Leask, Neel Nanda, Noura Al Moubayed
Sparse autoencoders (SAEs) are a popular method for decomposing Large Langage Models (LLM) activations into interpretable latents. However, due to their substantial training cost,…
cs.LG2025
Sparse Autoencoders Do Not Find Canonical Units of Analysis
Patrick Leask, Bart Bussmann, Michael Pearce +5
A common goal of mechanistic interpretability is to decompose the activations of neural networks into features: interpretable properties of the input computed by the model. Sparse…