4 accepted papers
Large language models (LLMs) can be controlled at inference time through prompts (in-context learning) and internal activations (activation steering). Different accounts have been proposed to explain these methods, yet their common goal of controlling model behavior raises the question of whether th…
Do the chains of thought (CoT) of reasoning Large Language Models (LLMs) reflect their internal computation? In this paper, we provide evidence of \textit{performative} CoT, where a model becomes strongly confident in its final answer, but continues generating excess tokens without revealing its int…
As large language models (LLMs) increasingly power conversational agents, understanding how they model users' emotional states is critical for ethical deployment. Inspired by emotion wheels, i.e., a psychological framework that argues emotions organize hierarchically, we analyze probabilistic depend…
A central aim of interpretability tools applied to language models is to recover meaningful concepts from model activations. Existing feature extraction methods focus on single activations regardless of the context, implicitly assuming independence (and therefore stationarity). This leaves open whet…