Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages (2023)

Abstract: Chain-of-thought (CoT) is capable of eliciting models to explicitly generate reasoning paths, thus promoting reasoning accuracy and attracting increasing attention. Specifically, zero-shot CoT achieves remarkable improvements in a wide range of reasoning tasks by simply instructing the LLM with the prompt “Let’s think step by step!”. Despite the success of zero-shot CoT, the existing zero-shot prompting techniques remain limited to a single language, making it challenging to generalize to other languages and hindering global development. In this work, we introduce cross-lingual prompting (CLP), aiming to improve zero-shot CoT reasoning across languages. Specifically, CLP consists of two main components: (1) cross-lingual alignment prompting and (2) task-specific solver prompting. The cross-lingual alignment prompting is responsible for aligning representations across different languages, whereas the task-specific solver prompting is used to generate the final chain of thoughts and results for the reasoning task. In addition, we further introduce cross-lingual self-consistent prompting (CLSP) to ensemble different reasoning paths across languages. Our experimental evaluations on several benchmarks demonstrate that CLP and CLSP significantly outperform the existing prompting methods and achieve state-of-the-art performance. We hope this work will inspire further breakthroughs in cross-lingual CoT.

## introduction large language models (llms) have shown remarkable success across various nlp tasks @xcite @xcite . unlike the previous pre-trained language models (plms) @xcite , llms are capable of achieving zeroshot learning without the need to modify the model parameters during the training and testing process, which gains increasing attention. specifically, zeroshot chain-of-thought (cot) @xcite only needs to append the prompt "let's think step by step!", which can elicit strong reasoning capabilities from large language models and demonstrate promising performance on various tasks, including arithmetic reasoning, commonsense reasoning @xcite and even robotic planning @xcite . take a traditional cot in figure 1 (a) as an example, a trigger prompt "let's think step by step!" is provided along with an english request to perform step-by-step reasoning. eventually, llms produce the corresponding answer "68 years". in fact, there are over 200 countries and 7,000 languages worldwide. with the acceleration of globalization, there is an urgent need for generalizing the current cot across different languages. despite the remarkable success of zero-shot cot, its reasoning abilities still struggle to generalize to different languages. @xcite introduce the first multi-lingual dataset to evaluate the mathematical reasoning capabilities of language models to facilitate the research of cross-lingual cot. unlike traditional cot scenarios, where the language of the request and cot output is the same, crosslingual cot requires the llm to generate cot in english for any given language by providing a trigger sentence "let's think in english step by step!", which is illustrated in figure 1 @xcite . unfortunately, little attention has been paid to zero-shot cross-lingual cot. to generalize the current cot across languages, we propose a novel cross-lingual prompting (clp), which aims to effectively bridge the gap across different languages. it consists of two components: experimental results reveal that clp achieves the sota performance by outperforming all baselines with a gain of over 1.8%. in addition, clsp can further enhance the performance by integrating knowledge across different languages. the main contributions of this work are concluded as follows: we hope this work can inspire further research on cross-lingual cot and the code are available at cross-lingual-prompting. ## background this section describes the definition of traditional and cross-lingual chain-of-thought. ## cross-lingual prompting to elicit the cross-lingual reasoning ability of llm, we introduce cross-lingual prompting (clp) as a solution. specifically, clp consists of two components: (1) cross-lingual alignment prompting ( §3.1) and (2) task-specific solver prompting ( §3.2). ## conclusion in this work, we introduced cross-lingual prompting (clp) for cross-lingual chain-of-thought. specifically, clp consists of cross-lingual alignment prompting and task-specific solver prompting to align representations across languages and generate the final reasoning paths in cross-lingual settings. in addition, we proposed a cross-lingual selfconsistent prompting (clsp) to effectively leverage knowledge across languages, which further boosts performance over clp. extensive experiments reveal that both clp and clsp can attain promising performance in cross-lingual cot.