FinePrompt: Unveiling the Role of Finetuned Inductive Bias on Compositional Reasoning in GPT-4
Jeong-Hwan Kim, Giwon Hong, Sung-Hyon Myaeng, Joyce Jiyoung Whang · 2023
Compositional reasoning across texts has been a long-standing challenge in natural language processing.With large language models like GPT-4 taking over the field, prompting techniques such as chain-of-thought (CoT) were proposed to unlock compositional, multi-step reasoning capabilities of LLMs.Despite their success, the prompts demand significant human effort to discover and validate them.Our work draws attention to the idea of transferring task-specific inductive biases from finetuned models to prompts, as a way of improving GPT-4's compositional reasoning capabilities.To leverage these inductive biases, we formulate prompt templates to ease the transfer of inductive biases.The experimental results on multi-hop question answering and numerical reasoning over text show that our proposed prompt scheme shows competitive zero-shot and few-shot performances compared to existing prompts on complicated reasoning tasks, highlighting the importance of adopting the validated biases of the previous paradigm.1