LogiCoT: Logical Chain-of-Thought Instruction Tuning
Hanmeng Liu, Zhiyang Teng, Leyang Cui, Chaoli Zhang, Qiji Zhou, Yue Zhang · 2023
Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability.Recent work on self-instruction tuning, such as Alpaca, has focused on enhancing the general proficiency of models.These instructions enable the model to achieve performance comparable to GPT-3.5 on general tasks like open-domain text generation and paraphrasing.However, they fall short of helping the model handle complex reasoning tasks.To bridge the gap, this paper presents LogiCoT, a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4.We elaborate on the process of harvesting instructions for prompting GPT-4 to generate chainof-thought rationales.LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.