Over the last decade, a wide range of training and deployment strategies for
Large Language Models (LLMs) have emerged. Among these, the prompting paradigms
of Auto-regressive LLMs (AR-LLMs) have catalyzed a significant surge in
Artificial Intelligence (AI). This paper aims to emphasize the significance of
utilizing free-form modalities (forms of input and output) and verbal free-form
contexts as user-directed channels (methods for transforming modalities) for
downstream deployment. Specifically, we analyze the structure of modalities
within both two types of LLMs and six task-specific channels during deployment.
From the perspective of users, our analysis introduces and applies the
analytical metrics of task customizability, transparency, and complexity to
gauge their usability, highlighting the superior nature of AR-LLMs' prompting
paradigms. Moreover, we examine the stimulation of diverse cognitive behaviors
in LLMs through the adoption of free-form text and verbal contexts, mirroring
human linguistic expressions of such behaviors. We then detail four common
cognitive behaviors to underscore how AR-LLMs' prompting successfully imitate
human-like behaviors using this free-form modality and channel. Lastly, the
potential for improving LLM deployment, both as autonomous agents and within
multi-agent systems, is identified via cognitive behavior concepts and
principles.

过去十年中，许多训练和部署大型语言模型（LLMs）的策略涌现出来。本文旨在强调利用自由形式的模态和口头自由形式的语境作为用户指导通道以进行下游部署的重要性。具体而言，我们分析了两种类型的 LLMs 的模态结构以及部署过程中的六个具体任务通道。从用户的角度出发，我们引入并应用了任务可定制性、透明度和复杂性的分析度量标准来衡量它们的可用性，突显了 AR-LLMs 的提示范例的优越性。此外，我们通过采用自由形式文本和口头语境来激发 LLMs 中的多样化认知行为，模仿了人类的这些行为的语言表达方式。然后，我们详细介绍了四种常见的认知行为，以强调 AR-LLMs 的提示如何成功地模仿使用这种自由形式的模态和通道的类似人类的行为。最后，通过认知行为的概念和原则，确定了改进 LLM 在自主代理和多代理系统中部署的潜力。

重新思考 ChatGPT 的成功：基于自回归语言模型的提示促使下的可用性和认知行为

Rethinking ChatGPT's Success: Usability and Cognitive Behaviors Enabled  by Auto-regressive LLMs' Prompting

There is considerable confusion about the role of Large Language Models
(LLMs) in planning and reasoning tasks. On one side are over-optimistic claims
that LLMs can indeed do these tasks with just the right prompting or
self-verification strategies. On the other side are perhaps over-pessimistic
claims that all that LLMs are good for in planning/reasoning tasks are as mere
translators of the problem specification from one syntactic format to another,
and ship the problem off to external symbolic solvers. In this position paper,
we take the view that both these extremes are misguided. We argue that
auto-regressive LLMs cannot, by themselves, do planning or self-verification
(which is after all a form of reasoning), and shed some light on the reasons
for misunderstandings in the literature. We will also argue that LLMs should be
viewed as universal approximate knowledge sources that have much more
meaningful roles to play in planning/reasoning tasks beyond simple
front-end/back-end format translators. We present a vision of {\bf LLM-Modulo
Frameworks} that combine the strengths of LLMs with external model-based
verifiers in a tighter bi-directional interaction regime. We will show how the
models driving the external verifiers themselves can be acquired with the help
of LLMs. We will also argue that rather than simply pipelining LLMs and
symbolic components, this LLM-Modulo Framework provides a better neuro-symbolic
approach that offers tighter integration between LLMs and symbolic components,
and allows extending the scope of model-based planning/reasoning regimes
towards more flexible knowledge, problem and preference specifications.

大语言模型在规划和推理任务中的作用有很多混淆。我们认为，自回归 LLM 本身无法进行规划或自验证，并指出了文献中常见误解的原因。我们提出了 LLM - 模块化框架的愿景，将 LLM 的优势与外部基于模型的验证器在更紧密的双向交互方式中结合起来。我们将展示如何利用 LLM 获得驱动外部验证器的模型。我们还将论述 LLM - 模块化框架提供了更好的神经符号方法，通过更灵活的知识、问题和偏好规范，扩展了基于模型的规划 / 推理范围。