提出可泛化理解度度量，让智能体更懂人类意图。
A Measure Based Generalizable Approach to Understandability
- 基于认知科学构建通用理解度度量框架
- 相比传统方法提升人类对智能体的控制力
- 适合需要高可解释性的交互系统研究者
成功的智能体-人类协作要求智能体生成的信息对人类可理解，且人类能轻松引导智能体达成目标。当前主流智能体（包括大语言模型）缺乏对理解度的精细把握，仅从训练数据中捕捉平均人类感受，导致可引导性有限（如需复杂的提示工程）。本文主张不依赖数据，而是建立可泛化、跨领域的理解度度量，作为智能体的行动指令。我们系统梳理了不同领域中理解度度量的研究，奠定以认知科学为基础的统一研究基础，推动未来更连贯、普适的理解度研究。
原文摘要 · Abstract (English)
Successful agent-human partnerships require that any agent generated information is understandable to the human, and that the human can easily steer the agent towards a goal. Such effective communication requires the agent to develop a finer-level notion of what is understandable to the human. State-of-the-art agents, including LLMs, lack this detailed notion of understandability because they only capture average human sensibilities from the training data, and therefore afford limited steerability (e.g., requiring non-trivial prompt engineering). In this paper, instead of only relying on data, we argue for developing generalizable, domain-agnostic measures of understandability that can be used as directives for these agents. Existing research on understandability measures is fragmented, we survey various such efforts across domains, and lay a cognitive-science-rooted groundwork for more coherent and domain-agnostic research investigations in future.
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