Foundation models, such as Large language Models (LLMs), have attracted
significant amount of interest due to their large number of applications.
Existing works show that appropriate prompt design, such as Chain-of-Thoughts,
can unlock LLM's powerful capacity in diverse areas. However, when handling
tasks involving repetitive sub-tasks and/or deceptive contents, such as
arithmetic calculation and article-level fake news detection, existing
prompting strategies either suffers from insufficient expressive power or
intermediate errors triggered by hallucination. To make LLM more discerning to
such intermediate errors, we propose to guide LLM with a Divide-and-Conquer
program that simultaneously ensures superior expressive power and disentangles
task decomposition, sub-task resolution, and resolution assembly process.
Theoretic analysis reveals that our strategy can guide LLM to extend the
expressive power of fixed-depth Transformer. Experiments indicate that our
proposed method can achieve better performance than typical prompting
strategies in tasks bothered by intermediate errors and deceptive contents,
such as large integer multiplication, hallucination detection and
misinformation detection.

建议使用分治程序来引导大型语言模型（LLM），以解决存在中间错误和欺骗内容的任务，可以提供更好的性能。