  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.
