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.

翻译：大语言模型（LLMs）的快速发展引发了一个问题：这些模型是否具有某种形式的意识。为应对这一挑战，Butlin等人（2023）基于神经科学理论提出了一个人工系统意识指标列表。在本研究中，我们评估了该列表中的一个关键指标——HOT-3，该指标用于测试由通用信念形成与行动选择系统引导的能动性，该系统通过元认知监控更新信念。我们将信念视为模型潜在空间中响应给定输入而涌现的表征，并引入一个量化其在生成过程中主导性的度量指标。通过分析不同模型与任务中竞争性信念之间的动态关系，我们揭示了三个关键发现：（1）外部操作能系统性地调节内部信念形成；（2）信念形成对模型行动选择具有因果驱动作用；（3）模型能够监控并报告自身的信念状态。这些结果共同为LLMs中存在信念引导的能动性与元认知监控提供了实证支持。更广泛而言，我们的研究为探索LLMs中能动性、信念和元认知的涌现奠定了方法论基础。