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- CAMEL: Communicative Agents for Mind Exploration of Large Language . . .
This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents, and provides insight into their "cognitive" processes
- CAMEL: Communicative Agents for Mind Exploration of . . . - Guohao Li
CAMEL: Communicative Agents for ''Mind'' Exploration of Large Language Model Society Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, Bernard Ghanem
- CAMEL | Proceedings of the 37th International Conference on Neural . . .
We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of a society of agents, providing a valuable resource for investigating conversational language models
- CAMEL: Communicative Agents for Mind Exploration of Large Language . . .
This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents, and provides insight into their “cognitive” processes
- CAMEL: Communicative Agents for “Mind” Exploration of Large Scale . . .
Our work presents a novel approach to the "mind exploration" of conversational agents By enabling these agents to communicate and collaborate in solving tasks, we gain insight into their actions and behaviors within a task-solving context
- CAMEL: Communicative Agents for Mind Exploration of Large Language . . .
This paper proposes a novel communicative agent framework named role-playing, using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions, and conducts comprehensive studies on instruction-following cooperation in multi-agent settings
- Guohao Li
Guohao Li is an artificial intelligence researcher and an open-source contributor working on building intelligent agents that can perceive, learn, communicate, reason, and act
- CAMEL: Communicative Agents for “Mind” Exploration of Large Language . . .
We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of a society of agents, providing a valuable resource for investigating conversational language models
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