The rapid evolution of large language models (LLMs) and their capacity to simulate human cognition and behavior has given rise to LLM-based frameworks and tools that are evaluated and applied based on their ability to perform tasks traditionally performed by humans, namely those involving cognition, decision-making, and social interaction. This survey provides a comprehensive examination of such human-centric LLM capabilities, focusing on their performance in both individual tasks (where an LLM acts as a stand-in for a single human) and collective tasks (where multiple LLMs coordinate to mimic group dynamics). We first evaluate LLM competencies across key areas including reasoning, perception, and social cognition, comparing their abilities to human-like skills. Then, we explore real-world applications of LLMs in human-centric domains such as behavioral science, political science, and sociology, assessing their effectiveness in replicating human behaviors and interactions. Finally, we identify challenges and future research directions, such as improving LLM adaptability, emotional intelligence, and cultural sensitivity, while addressing inherent biases and enhancing frameworks for human-AI collaboration. This survey aims to provide a foundational understanding of LLMs from a human-centric perspective, offering insights into their current capabilities and potential for future development.

翻译：大型语言模型（LLMs）的快速发展及其模拟人类认知与行为的能力，催生了基于LLM的框架与工具。这些框架与工具的评价与应用，主要依据其执行传统上由人类完成的任务的能力，即涉及认知、决策和社交互动的任务。本综述对此类以人为中心的LLM能力进行了全面审视，重点关注其在个体任务（单个LLM作为单个人的替代）和集体任务（多个LLM协同以模拟群体动态）中的表现。我们首先评估了LLM在推理、感知和社会认知等关键领域的能力，并将其与类人技能进行比较。接着，我们探讨了LLM在行为科学、政治学和社会学等以人为中心领域的实际应用，评估其在复现人类行为与互动方面的有效性。最后，我们指出了挑战与未来的研究方向，例如改进LLM的适应性、情商和文化敏感性，同时解决其固有偏见并增强人机协作框架。本综述旨在从以人为中心的视角提供对LLM的基础性理解，为其当前能力与未来发展潜力提供见解。