LogiCoT: Logical Chain-of-Thought Instruction-Tuning
This addresses a gap in self-instruction tuning for improving reasoning abilities in AI models, though it appears incremental as it builds on existing methods like Alpaca.
The paper tackles the problem of enhancing complex reasoning in language models by introducing LogiCoT, a new instruction-tuning dataset for logical chain-of-thought reasoning, which helps models handle such tasks more effectively.
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 chain-of-thought rationales. LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.