Agent | Jiangjie Chen

From Persona to Personalization: A Survey on Role-Playing Language Agents

This paper surveys Role-Playing Language Agents (RPLAs) by categorizing personas, discussing their development, and examining their applications, challenges, and future directions.

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, Caiyu Hu, Siye Wu, Scott Ren, Ziquan Fu, Yanghua Xiao

From Persona to Personalization: A Survey on Role-Playing Language Agents

Character is Destiny: Can Large Language Models Simulate Persona-Driven Decisions in Role-Playing?

We evaluate the potential of LLMs to make decisions as literary characters, using a new dataset and improved method that enhances decision-making accuracy, with future work and resources to be shared publicly.

Rui Xu, Xintao Wang, Jiangjie Chen, Siyu Yuan, Xinfeng Yuan, Jiaqing Liang, Zulong Chen, Xiaoqing Dong, Yanghua Xiao

Character is Destiny: Can Large Language Models Simulate Persona-Driven Decisions in Role-Playing?

Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works

We propose a new approach to evaluate LLMs’ understanding of fictional characters by summarizing character profiles, using a specially constructed dataset, and shows promising results for their application in role-playing agents.

Xinfeng Yuan, Siyu Yuan, Yuhan Cui, Tianhe Lin, Xintao Wang, Rui Xu, Jiangjie Chen, Deqing Yang

Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works

SurveyAgent: A Conversational System for Personalized and Efficient Research Survey

We propose a novel conversational AI system that enhances researchers’ literature review processes by providing personalized knowledge management, literature recommendations, and query answering through a unified platform.

Xintao Wang, Jiangjie Chen, Nianqi Li, Lida Wang, Xinfeng Yuan, Wei Shi, Xuyang Ge, Rui Xu, Yanghua Xiao

SurveyAgent: A Conversational System for Personalized and Efficient Research Survey

Agent Group Chat: An Interactive Group Chat Simulacra For Better Eliciting Collective Emergent Behavior

We propose a simulation to study language’s influence on collective behavior by having agents engage in free chat within various narrative scenarios, with findings suggesting that greater information exchange promotes more orderly and meaningful emergent behaviors.

Zhouhong Gu, Xiaoxuan Zhu, Haoran Guo, Lin Zhang, Yin Cai, Hao Shen, Jiangjie Chen, Zheyu Ye, Yifei Dai, Yan Gao, Yao Hu, Hongwei Feng, Yanghua Xiao

Agent Group Chat: An Interactive Group Chat Simulacra For Better Eliciting Collective Emergent Behavior

TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation

TimeArena enhances LLMs with temporal dynamics for better multitasking, showing advanced models like GPT-4 still trail behind human temporal awareness.

Yikai Zhang, Siyu Yuan, Caiyu Hu, Kyle Richardson, Yanghua Xiao, Jiangjie Chen

TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation

InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews

We propose InCharacter, a method using psychological scales to evaluate the personality fidelity of role-playing agents (RPAs) powered by large language models.

Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao

InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews

TravelPlanner: A Benchmark for Real-World Planning with Language Agents

We introduced TravelPlanner, a benchmark for assessing language agents’ planning abilities, showing that even advanced models like GPT-4 face difficulties with complex tasks.

Jian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu, Renze Lou, Yuandong Tian, Yanghua Xiao, Yu Su

TravelPlanner: A Benchmark for Real-World Planning with Language Agents

EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction

We proposes EASYTOOL, a method that simplifies tool documentation into concise instructions, improving tool use by language models.

Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Ren Kan, Dongsheng Li, Deqing Yang

EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction

Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena

We propose AucArena to tests LLMs in auctions, showing they can strategize but with variable success, indicating potential for enhancement.

Jiangjie Chen, Siyu Yuan, Rong Ye, Bodhisattwa Prasad Majumder, Kyle Richardson

Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena