The performance of Large Language Models (LLMs) in reasoning tasks depends
heavily on prompt design, with Chain-of-Thought (CoT) and self-consistency
being critical methods that enhance this ability. However, these methods do not
fully exploit the answers generated by the LLM to guide subsequent responses.
This paper proposes a new prompting method, named Progressive-Hint Prompting
(PHP), that enables automatic multiple interactions between users and LLMs by
using previously generated answers as hints to progressively guide toward the
correct answers. PHP is orthogonal to CoT and self-consistency, making it easy
to combine with state-of-the-art techniques to further improve performance. We
conducted an extensive and comprehensive evaluation to demonstrate the
effectiveness of the proposed method. Our experimental results on six
benchmarks show that combining CoT and self-consistency with PHP significantly
improves accuracy while remaining highly efficient. For instance, with
text-davinci-003, we observed a 4.2% improvement on GSM8K with greedy decoding
compared to Complex CoT, and a 46.17% reduction in sample paths with
self-consistency. With GPT-4 and PHP, we achieve state-of-the-art performances
on SVAMP (91.9%), GSM8K (95.5%) and AQuA (79.9%).Comment: Tech Repor