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

arXiv: https://arxiv.org/abs/2405.10474 · HTML: https://mlsanity.com/paper/2405.10474/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 2 of its 47 references that are in the map.
- First posted on arXiv 17 May 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 2 of 2)

- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://mlsanity.com/paper/2304.06364/) (2023)

## Built on by (most cited first, 1 of 1)

- ["I made this (sort of)": Negotiating authorship, confronting fraudulence, and exploring new musical spaces with prompt-based AI music generation](https://mlsanity.com/paper/2507.23365/) (2025)

## Abstract (arXiv)

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.