  Most existing prompting methods suffer from the issues of generalizability
and consistency, as they often rely on instance-specific solutions that may not
be applicable to other instances and lack task-level consistency across the
selected few-shot examples. To address these limitations, we propose a
comprehensive framework, StrategyLLM, allowing LLMs to perform inductive
reasoning, deriving general strategies from specific task instances, and
deductive reasoning, applying these general strategies to particular task
examples, for constructing generalizable and consistent few-shot prompts. It
employs four LLM-based agents: strategy generator, executor, optimizer, and
evaluator, working together to generate, evaluate, and select promising
strategies for a given task. Experimental results demonstrate that StrategyLLM
outperforms the competitive baseline CoT-SC that requires human-annotated
solutions on 13 datasets across 4 challenging tasks without human involvement,
including math reasoning (34.2\% $\rightarrow$ 38.8\%), commonsense reasoning
(70.3\% $\rightarrow$ 72.5\%), algorithmic reasoning (73.7\% $\rightarrow$
85.0\%), and symbolic reasoning (30.0\% $\rightarrow$ 79.2\%). Further analysis
reveals that StrategyLLM is applicable to various LLMs and demonstrates
advantages across numerous scenarios.
