Instructional LLMs and Prompt Engineering. LLMs are trained on diverse text data and excel in
text completion, with various downstream NLP applications [9, 14, 30, 75, 69]. However, InstructGPT
suggests that LLMs may not align with user intent, proposing reinforcement learning from human
feedback (RLHF) [15] and Instruction Fine-Tuning (IFT) [72] to improve LLMs’ relevance and
appropriateness to user instructions. Chain-of-Thought (CoT) [73] and zero-shot-CoT [37] are special
types of instruction that signiﬁcantly enhance LLMs’ performance on reasoning and arithmetic tasks.
These techniques underpin the impressive capabilities of recent dialogue LLMs [61, 68, 22, 6, 47, 10],
which aim to simulate human-like conversations and provide personalized and interactive experiences
for users, exhibiting the behavior of all three conversational AI agents [21]. However, generating
instruction datasets is a crucial challenge in building instruct-based LLMs, with existing datasets
CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society