  Cross-lingual chain-of-thought can effectively complete reasoning tasks
across languages, which gains increasing attention. Recently, dominant
approaches in the literature improve cross-lingual alignment capabilities by
integrating reasoning knowledge from different languages. Despite achieving
excellent performance, current methods still have two main challenges: (1)
Manual language specification: They still highly rely on manually selecting the
languages to integrate, severely affecting their generalizability; (2) Static
weight allocation: Current methods simply integrate all languages equally. In
fact, different language reasoning paths should have different weights to
achieve better complementation and integration. Motivated by this, we introduce
an Automatic Cross-lingual Alignment Planning (AutoCAP) for zero-shot
chain-of-thought to address the above challenges. The core of AutoCAP consists
of two components: (1) Automatic Language Selection Prompting to guide LLMs to
select appropriate languages and (2) Automatic Weight Allocation Prompting to
automatically allocate alignment weight scores to each reasoning path.
Extensive experiments on several benchmarks reveal that AutoCAP achieves
state-of-the-art performance, surpassing previous methods that required manual
effort.
