generated 50 assistant roles, 50 user roles, and 10 tasks for each combination of roles yielding a
total of 25,000 conversations. The generated assistant roles and user roles are shown in Figure 5 (AI
Society).
Code: To generate the Code dataset, we use a scalable approach similar to that of the AI Society
dataset. Firstly, we prompt the LLM agent to provide us with a list of programming languages and
domains. Then, we ask the LLM agent to generate a set of tasks that an expert programmer in a
speciﬁc programming language can collaborate with a person working in a speciﬁc domain to solve.
The task is then made more speciﬁc using our task speciﬁer prompt. The prompts for language
Generation, domain Generation, and task generation are shown in Figure 4 (Code). For our Code
dataset, we generated 20 programming languages, 50 domains, and 50 tasks for each combination
of language and domains yielding a total of 50,000 conversations. The generated programming
CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society