  Large Language Models (LLMs) have recently showcased remarkable
generalizability in various domains. Despite their extensive knowledge, LLMs
still face challenges in efficiently utilizing encoded knowledge to develop
accurate and logical reasoning processes. To mitigate this problem, we
introduced Hint-before-Solving Prompting (HSP), which guides the model to
generate hints (e.g., specific knowledge or key ideas) for solving the problem
and then generate solutions containing intermediate reasoning steps. Since HSP
is orthogonal to prompting methods (e.g., Chain-of-Thought (CoT)), we applied
HSP to CoT, Least-to-Most, Plan-and-Solve, and Standard promptings. The results
of extensive experiments on 6 reasoning benchmarks and 4 open-source LLMs
demonstrate that HSP can effectively improve the accuracy of reasoning tasks:
(1) By applying high-quality hint-enhanced HSP to CoT prompting,
Llama2-70B-Chat shows an improvement of 9.7. (2) Beyond exploring training-free
LLM capabilities, we built the HSPMATH dataset based on HSP and fine-tuned
Llemma-7B, reaching 64.3 accuracy, surpassing GPT-3.5 and WizardMath-13B. We
make our code and dataset publicly available at
\url{https://github.com/jinlanfu/HSP}.
