Recent research shows that Large Language Models (LLMs) exhibit a compelling
level of proficiency in Theory of Mind (ToM) tasks. This ability to impute
unobservable mental states to others is vital to human social cognition and may
prove equally important in principal-agent relations between individual humans
and Artificial Intelligences (AIs). In this paper, we explore how a mechanism
studied in developmental psychology known as Violation of Expectation (VoE) can
be implemented to reduce errors in LLM prediction about users by leveraging
emergent ToM affordances. And we introduce a \textit{metacognitive prompting}
framework to apply VoE in the context of an AI tutor. By storing and retrieving
facts derived in cases where LLM expectation about the user was violated, we
find that LLMs are able to learn about users in ways that echo theories of
human learning. Finally, we discuss latent hazards and augmentative
opportunities associated with modeling user psychology and propose ways to
mitigate risk along with possible directions for future inquiry.

大语言模型（LLMs）在心灵理论（ToM）任务中表现出令人信服的水平，在人际认知中将不可观察的心理状态归因于他人是人类社会认知的关键之一，也在人与人工智能（AI）之间的委托关系中可能同样重要。本文探讨了一种发展心理学中研究的期望违反（VoE）机制如何通过利用出现的 ToM 便利来减少 LLM 对用户预测中的错误，并引入了一种	extit {元认知提示} 框架，将 VoE 应用于 AI 导师的背景中。通过存储和检索在 LLM 对用户的预期被违反的情况下得出的事实，我们发现 LLMs 能够以类似人类学习理论的方式了解用户。最后，我们讨论了建模用户心理的潜在风险和增强机会，并提出了减少风险的方法以及未来研究的可能方向。

通过元认知提示违背预期减少大型语言模型中心理理论预测误差

Violation of Expectation via Metacognitive Prompting Reduces Theory of  Mind Prediction Error in Large Language Models

In Large Language Models (LLMs), there have been consistent advancements in
task-specific performance, largely influenced by effective prompt design. While
recent research on prompting has enhanced the reasoning capabilities of LLMs, a
gap remains in further improving their understanding abilities. In this study,
we introduce metacognitive prompting (MP), a strategy inspired by human
introspective reasoning processes. Using MP, LLMs undergo a systematic series
of structured, self-aware evaluations, drawing on both their vast inherent
knowledge and new insights. Our experiments involve five prevalent LLMs:
Llama2, Vicuna, PaLM, GPT-3.5, and GPT-4, all of which span various general
natural language understanding (NLU) tasks from the GLUE and SuperGLUE
benchmarks. Results indicate that, although GPT-4 consistently excels in most
tasks, PaLM, when equipped with MP, approaches its performance level.
Furthermore, across models and datasets, MP consistently outperforms existing
prompting methods, including standard and chain-of-thought prompting. This
study underscores the potential to amplify the understanding abilities of LLMs
and highlights the benefits of mirroring human introspective reasoning in NLU
tasks.

通过使用元认知提示，通过系统性的结构化、自我意识评估，结合大量内在的知识和新的认识，可以提高大型语言模型的理解能力。实验结果表明，元认知提示始终优于现有的提示方法，并通过提高 GPT-4 的性能水平，增强了 GPT-4 在各种自然语言理解任务中的表现。