用角色设定让大模型生成有社会智能的对话行为
LLM-enabled Social Agents
- 以角色描述定义智能体身份，实现语言能力向社会行为转化
- 强调角色、规范与情境约束对社交行为的关键作用
- 适合研究社会智能代理、人机交互与角色建模的学者
大型语言模型（LLMs）通过自然语言使软件、物理及仿真智能体能够进行交流与协商，从而变革了人机及机间互动。然而，流利的语言表达并不自动带来社会可理解的行为。当前多数系统在角色、规范、意图和情境约束方面仍缺乏坚实基础，限制了其在社会环境中的有效参与。本文提出一个概念性基准：LLM驱动的社会智能体应基于通过角色描述具体化的角色定义。在此基础上，本文勾勒出表示学习、混合控制与评估方面的研究方向。结论指出，基于角色的定义是将语言能力转化为社会行为的必要基础。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have transformed agent-agent and human-agent interaction by enabling software, physical, and simulation agents to communicate and deliberate through natural language. Yet fluent language use does not by itself yield socially intelligible behaviour. Most current systems remain weakly grounded in roles, norms, intentions, and contextual constraints, limiting their capacity for meaningful participation in social environments. This paper develops a conceptual baseline for LLM-enabled social agents by arguing that they should be grounded in role definitions operationalized through persona descriptions. On this basis, we outline research directions for representation, hybrid control, and evaluation. The paper concludes that persona-based role definitions are a necessary foundation for turning language competence into social behaviour.
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