让对话AI学会长期合作，提升任务协作能力
Social Cooperation in Conversational AI Agents
- 引入博弈论框架建模人类长期互动策略
- 在长周期交互中显著提升错误修正与协作效率
- 适合研究长期人机协作与智能体社会性设计的学者
基于大语言模型的对话AI助手在写作、编程、设计等任务中展现出广泛应用潜力。然而，由于训练数据主要来自短期人类交互，这类模型在处理长期互动时表现不佳，例如用户反复纠正错误的情境。本文提出，通过显式建模人类的社会智能——即建立和维护长期关系的能力——可解决该问题。我们以数学方式模拟人类在长时间交流中用于相互沟通与推理的策略，进而推导出可用于优化大语言模型及未来智能体的新博弈论目标。
原文摘要 · Abstract (English)
The development of AI agents based on large, open-domain language models (LLMs) has paved the way for the development of general-purpose AI assistants that can support human in tasks such as writing, coding, graphic design, and scientific research. A major challenge with such agents is that, by necessity, they are trained by observing relatively short-term interactions with humans. Such models can fail to generalize to long-term interactions, for example, interactions where a user has repeatedly corrected mistakes on the part of the agent. In this work, we argue that these challenges can be overcome by explicitly modeling humans' social intelligence, that is, their ability to build and maintain long-term relationships with other agents whose behavior cannot always be predicted. By mathematically modeling the strategies humans use to communicate and reason about one another over long periods of time, we may be able to derive new game theoretic objectives against which LLMs and future AI agents may be optimized.
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