Belief Dynamics Reveal the Dual Nature of In-Context Learning and Activation Steering

Recent research published on arXiv investigates the influence of prompts and internal activations on large language models, focusing on two key mechanisms: in-context learning and activation steering. These methods have been shown to affect model behavior, and the study proposes that they may be components of a broader, unified framework for controlling such behavior. By examining the belief dynamics underlying these approaches, the research aims to deepen understanding of how large language models process and respond to information. This work builds on ongoing efforts to explore the capabilities and limitations of large language models, as reflected in related studies within the same domain. The proposed unification of in-context learning and activation steering could provide a more comprehensive perspective on model manipulation techniques. Overall, the study contributes to the evolving discourse on enhancing the interpretability and controllability of artificial intelligence systems.