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From Persona to Personalization: A Survey on Role-Playing Language Agents
April 30, 2024, 4:50 a.m. | Jiangjie Chen, Xintao Wang, Rui Xu, Siyu Yuan, Yikai Zhang, Wei Shi, Jian Xie, Shuang Li, Ruihan Yang, Tinghui Zhu, Aili Chen, Nianqi Li, Lida Chen, C
cs.CL updates on arXiv.org arxiv.org
Abstract: Recent advancements in large language models (LLMs) have significantly boosted the rise of Role-Playing Language Agents (RPLAs), i.e., specialized AI systems designed to simulate assigned personas. By harnessing multiple advanced abilities of LLMs, including in-context learning, instruction following, and social intelligence, RPLAs achieve a remarkable sense of human likeness and vivid role-playing performance. RPLAs can mimic a wide range of personas, ranging from historical figures and fictional characters to real-life individuals. Consequently, they have catalyzed …
abstract advanced agents ai systems arxiv context cs.ai cs.cl in-context learning intelligence language language models large language large language models llms multiple personalization personas playing role social specialized ai survey systems type
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