Zhangyue Yin, Qiushi Sun, Qipeng Guo, Zhiyuan Zeng, Xiaonan Li, Junqi Dai, Qinyuan Cheng, Xuanjing Huang, Xipeng Qiu. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Large language models (LLMs) can be used to serve as agents to simulate human behaviors, given the powerful ability to understand human instructions and provide high-quality generated texts.Such ability stimulates us to wonder whether LLMs can simulate a person in a higher form than simple human behaviors.Therefore, we aim to train an agent with the profile, experience, and emotional states of a specific person instead of using limited prompts to instruct ChatGPT API.In this work, we introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc.Our method focuses on editing profiles as experiences of a certain character and training models to be personal simulacra with these experiences.To assess the effectiveness of our approach, we build a test playground that interviews trained agents and evaluates whether the agents memorize their characters and experiences.Experimental results show interesting observations that help build future simulacra of humankind.1

Large Language Models (LLMs) have recently made significant strides in complex reasoning tasks through the Chain-of-Thought technique.Despite this progress, their reasoning is often constrained by their intrinsic understanding, lacking external insights.To address this, we propose Exchange-of-Thought (EoT), a novel framework that enables cross-model communication during problem-solving.Drawing inspiration from network topology, EoT integrates four unique communication paradigms: Memory, Report, Relay, and Debate.This paper delves into the communication dynamics and volume associated with each paradigm.To counterbalance the risks of incorrect reasoning chains, we implement a robust confidence evaluation mechanism within these communications.Our experiments across diverse complex reasoning tasks demonstrate that EoT significantly surpasses established baselines, underscoring the value of external insights in enhancing LLM performance.Furthermore, we show that EoT achieves these superior results in a cost-effective manner, marking a promising advancement for efficient and collaborative AI problem-solving."Two heads are better than one.

Hang Yan, Junqi Dai, Tuo Ji, Xipeng Qiu, Zheng Zhang. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Hang Yan, Tao Gui, Junqi Dai, Qipeng Guo, Zheng Zhang, Xipeng Qiu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Junqi Dai, Hang Yan, Tianxiang Sun, Pengfei Liu, Xipeng Qiu. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.