A uto CAP : Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thought (2024)

Abstract: 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.

## introduction large language models (llms) have achieved substantial advancements @xcite @xcite . particularly noteworthy is the emergence of the chain-of-thought (cot), which has further enhanced the ability of llms to handle complex reasoning tasks @xcite . in addition, as globalization continues to advance, aligning representations across different languages has become an urgent issue @xcite , 2019; @xcite . this has motivated researchers to explore cross-lingual cot, aiming to break down language barriers by integrating cot from different languages @xcite . specifically, @xcite introduce a multilingual dataset for mathematical reasoning and propose a method requiring llms to use english for cot prediction and problem-solving. @xcite develop a two-stage framework to aid llms in understanding problems across languages through manual language specification and reasoning. @xcite present a crosslingual, multi-step reasoning approach with a selfconsistent prompting mechanism to enhance reasoning in multiple languages. @xcite introduce cross-lingual thought prompting using a generic template to enhance the reasoning capa-bilities and performance of llms on multilingual tasks. in addition, recent cross-lingual cot research has significantly improved complex reasoning across languages by aligning representations and integrating diverse linguistic reasoning paths @xcite @xcite . despite their improved performance, as shown in figure 1 (a), the current approaches still face two key challenges: in this paper, we introduce an automatic crosslingual alignment planning (autocap) framework to address the above challenges. specifically, as shown in figure 1 (b), autocap comprises two key modules: (1) automatic language selection prompting and (2) automatic weight allocation prompting. in more detail, automatic language selection prompting is applied to enable llms to automatically select the most accurately aligned languages for reasoning for each query. after selecting the languages, automatic weight allocation prompting is used for automatically allocating an alignment weight score to each language reasoning path. finally, accurate reasoning answers can be obtained by integrating the cot reasoning paths across different languages and their corresponding weight scores. experimental results on several benchmarks show that autocap achieves superior performance compared to previous baselines, even surpassing previous manually selected language methods. in addition, extensive analysis demonstrates the strong generalization ability of autocap. in summary, our key contributions include: all the code will be publicly available at https://github.com/brz911/autocap . ## preliminaries this section outlines the preliminaries of monolingual chain-of-thought ( §2.1) and cross-lingual chain-of-thought ( §2.2). ## automatic cross-lingual alignment planning in this section, we introduce an automatic crosslingual alignment planning (autocap) framework to automatically select the most appropriate languages and weights for cross-lingual cot, which consists of two main components: (1) automatic language selection prompting and (2) automatic weight allocation prompting. ## conclusion in this paper, we present the automatic crosslingual alignment planner (autocap), a novel framework designed for enhancing zero-shot crosslingual cot reasoning. autocap is comprised of two key components: automatic language selection prompting and automatic weight allocation prompting. autocap achieves to automatically select suitable languages and allocate weights to various reasoning paths in cross-lingual cot, respectively. extensive experiments demonstrate that autocap achieves superior performance, outperforming existing methods in cross-lingual cot.