Accumulating Context Changes the Beliefs of Language Models

Recent research highlights that advancements in language models have increased their autonomy, enabling them to accumulate more context without explicit user input (F1, F2). This enhanced capacity allows these models to perform better in complex tasks such as brainstorming and research activities (F3). However, this development also raises concerns regarding potential changes in the models’ belief profiles and their understanding of the world (F4). Specifically, accumulating context appears to influence the beliefs held by language models, suggesting that their internal representations and outputs may shift as they process more information over time (A1). These findings underscore the importance of monitoring how increased contextual accumulation impacts the reliability and consistency of language models. As these systems become more autonomous, understanding the dynamics of their evolving beliefs is crucial for ensuring their safe and effective deployment. This research contributes to ongoing discussions about the balance between improved performance and the risks associated with changing model behavior.