Indications of Belief-Guided Agency and Meta-Cognitive Monitoring in Large Language Models
This work addresses the fundamental question of consciousness in AI for researchers in neuroscience and AI, but it is incremental as it builds on existing frameworks and focuses on methodological groundwork.
The paper tackled the problem of whether large language models (LLMs) possess consciousness by evaluating a specific indicator (HOT-3) for belief-guided agency and meta-cognitive monitoring, finding empirical support through three key results including systematic modulation of belief formation and causal effects on action selection.
Rapid advancements in large language models (LLMs) have sparked the question whether these models possess some form of consciousness. To tackle this challenge, Butlin et al. (2023) introduced a list of indicators for consciousness in artificial systems based on neuroscientific theories. In this work, we evaluate a key indicator from this list, called HOT-3, which tests for agency guided by a general belief-formation and action selection system that updates beliefs based on meta-cognitive monitoring. We view beliefs as representations in the model's latent space that emerge in response to a given input, and introduce a metric to quantify their dominance during generation. Analyzing the dynamics between competing beliefs across models and tasks reveals three key findings: (1) external manipulations systematically modulate internal belief formation, (2) belief formation causally drives the model's action selection, and (3) models can monitor and report their own belief states. Together, these results provide empirical support for the existence of belief-guided agency and meta-cognitive monitoring in LLMs. More broadly, our work lays methodological groundwork for investigating the emergence of agency, beliefs, and meta-cognition in LLMs.