Agency, the capacity to proactively shape events, is central to how humans interact and collaborate. While LLMs are being developed to simulate human behavior and serve as human-like agents, little attention has been given to the Agency that these models should possess in order to proactively manage the direction of interaction and collaboration. In this paper, we investigate Agency as a desirable function of LLMs, and how it can be measured and managed. We build on social-cognitive theory to develop a framework of features through which Agency is expressed in dialogue - indicating what you intend to do (Intentionality), motivating your intentions (Motivation), having self-belief in intentions (Self-Efficacy), and being able to self-adjust (Self-Regulation). We collect a new dataset of 83 human-human collaborative interior design conversations containing 908 conversational snippets annotated for Agency features. Using this dataset, we develop methods for measuring Agency of LLMs. Automatic and human evaluations show that models that manifest features associated with high Intentionality, Motivation, Self-Efficacy, and Self-Regulation are more likely to be perceived as strongly agentive.

翻译：能动性——即主动塑造事件的能力——是人类互动与协作的核心。尽管大语言模型正被开发用于模拟人类行为并充当类人智能体，但关于这些模型为了主动引导交互与协作方向而应具备何种能动性的研究尚属空白。本文从功能需求角度探究LLM的能动性，并探讨如何度量与管理这一特性。我们基于社会认知理论构建特征框架，通过对话中的意向性（表明意图）、动机性（激活意图）、自我效能感（对意图的自我信念）与自我调节（自我调整能力）四个维度表征能动性。我们收集了包含83段人际协作室内设计对话的新数据集，标注了908个对话片段的能动性特征。利用该数据集，我们开发了LLM能动性度量方法。自动评估与人工评估结果表明，在意向性、动机性、自我效能感与自我调节方面表现突出的模型更易被感知为强能动性。