A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages (2024)

Abstract: Reasoning methods, best exemplified by the well-known Chain-of-Thought (CoT), empower the reasoning abilities of Large Language Models (LLMs) by eliciting them to solve complex tasks in a step-by-step manner. Although they are achieving significant success, the ability to deliver multi-step reasoning remains limited to English because of the imbalance in the distribution of pre-training data, which makes other languages a barrier. In this paper, we propose Cross-lingual Tree-of-Thoughts (Cross-ToT), a method for aligning Cross-lingual CoT reasoning across languages. The proposed method, through a self-consistent cross-lingual prompting mechanism inspired by the Tree-of-Thoughts approach, provides multi-step reasoning paths in different languages that, during the steps, lead to the final solution. Experimental evaluations show that our method significantly outperforms existing prompting methods by reducing the number of interactions and achieving state-of-the-art performance.

## introduction chain-of-thought (cot) prompting elicits large language models (llms) to break down a reasoning task towards a sequence of intermediate steps @xcite . previous works have demonstrated that llms achieve impressive performances in zero-shot learning scenarios without the need to modify the model parameters during the training and testing process. in particular, by appending to the prompt "let's think step by step!" @xcite llms with at least several billions of parameters, such as gpts family (ope-nai, 2023) or palm @xcite , deliver multi-step controlled reasoning, achieving promising results across commonsense @xcite , symbolic and mathematical reasoning datasets @xcite . although the performances seem promising, they are only firmly established in english. this poses a barrier to generalizing current cot techniques to different languages. hence, despite the remarkable success of zero-shot cot techniques, the reasoning abilities of llms still struggle to generalize to different languages. @xcite introduced the first multilingual benchmark to assess llms' mathematical reasoning abilities using prompts in different languages. @xcite propose task-specific solver prompting, using a succession of prompts, elicit the llms to understand questions and deliver cot answers in different languages. however, these strategies require two-step prompts, which goes against the zero-shot approach. in this paper, we propose cross-lingual treeof-thoughts (cross-tot), a method for aligning cross-lingual cot reasoning across languages by proposing a cross-lingual alignment prompt to elicit the model to deliver a self-consistent chainof-thougt. our method is inspired by the tree-of-thoughts (tot) prompting @xcite that asks llms to perform decision-making by considering multiple different reasoning paths @xcite . in particular, our cross-tot is a tot-style prompting to deliver the reasoning process in different languages that, step-by-step, converge to a single final solution. the inherent insight is that as the different paths of thought evolve, the relationships between the different languages are inherently grasped via self-consistent chains-of-thougt. this leads to the target research questions, which are the focus of this paper: rq1: are llms able to deliver cross-lingual multi-step reasoned answers? rq2: are the different paths of tot evolving self-correcting each other? rq3: what is the role of english in crosslingual scenarios? to answer these questions, we propose cross-tot, a novel cross-lingual prompting strategy that aims to bridge the gap across different languages. in particular, using the prompt shown in figure 1 , we elicit the model to deliver different cot reasoning steps in different languages that converge to the final solution step-by-step. we test our method on gpt-3.5 and conduct an extensive analysis using multilingual grade school math (mgsm) @xcite , cross-lingual natural language inference (xnli) @xcite • we introduce cross-tot, which is a novel cross-lingual prompting mechanism that stimulates the model to produce parallel cot reasoning processes across different languages; ## cross-lingual multi-step reasoning to elicit the multi-step reasoning abilities of llms in cross-lingual scenarios, we propose cross-tot, which is a cross-lingual alignment chain-of-thought as a solution. in particular, our method overcomes the multi-lingual and cross-lingual approaches introduced in section 2.1. in fact, our approach elicits the llms to deliver selfconsistent parallel chain-of-thougts, introduced in section 2.2. ## main results mechanisms for delivering multistep-reasoned answers across languages can be empowered via cross-tot that align languages' chain-of-thoughts (cot). our approach based on a treeof-thoughts-inspired prompting mechanism (see figure 1 ) outperforms state-of-the-art prompting techniques on arithmetic reasoning tasks as shown in our approach outperforms the methods proposed in @xcite that are yet surpassed by the cross-cot proposed by @xcite . however, although cross-cot outperforms previous approaches, it is necessary to clarify which path, if any, leads to the correct reasoning (section 5.3), whether the introduction of english can increase performance (section 5.1) and finally the trade-off between the number of languages (in our case path) and the final results (section 5.2). ## analysis in this section, we explore the contribution of english in the cross-lingual prompt (in section 5.1), then study the impact of different languages on the final results (section 5.2) and the reasoning evolution (section 5.3) and close with an in-depth analysis of performance in different tasks in section 5.4. ## related work large language models (llms) with billions of parameters demonstrate in-context learning and few-shot learning abilities @xcite @xcite to guide llms to generate desired task responses, marking the advent of the prompting era and surpassing the age of the intermediate steps in algorithmic and structured reasoning @xcite . nevertheless, early works challenged the efficacy of few-shot techniques for empowering the prompting phase and downstream performances. in particular, @xcite refined the original idea of chain-of-thought (cot) @xcite by considering various reasoning paths as well known as tree-of-thought. the traditional and derivated cot mechanisms have achieved considerable success but are limited to generating answers within a single language (i.e., english). @xcite proposed a multilingual evaluation that @xcite extended to cross-lingual scenarios. in particular, @xcite proposed a prompt mechanism to handle requests in any language and generate cot specifically in english. this approach, which in our construct we called cross-cot has been proposed both single-phase, i.e., as a single prompt (clp) also adopted by @xcite and multiphase (clps) i.e., characterized by self-consistent prompts that follow the prompting methodology proposed in @xcite . although the mechanism achieves state-of-the-art cross-linguistic reasoning steps, the single-phase promting underperforms in low-resouces languages and the multiphase prompting characterized by a series of cascading prompts is supported far away from the zero-shot chain-of-thought concept. in our work, we propose a method of cot reasoning inspired. specifically, we elicit the crosslingual generation of a series of parallel crosslingual reasoning paths using a single prompt. in fact, our method is inspired by the tree-of-thoughts approach proposed by @xcite . hence, in a different way from previous approaches, our technique generates shared parallel reasoning paths that share the "thoughts process" delivering self-consistent answers and reducing reasoning steps. our work goes beyond in the following ways: ## future works in future work, we intend to incorporate smallerscale language models (slms) into our evaluations. however, the ability to produce multi-step reasoned answers is limited in slms. to address this, a range of techniques are emerging to align and transfer reasoning abilities between llms and slms @xcite . our aim is to enhance current alignment pipelines @xcite to enable cross-lingual reasoning capabilities across different languages and scenarios. including methods that emphasize the importance of language structure (zanzotto et al., 2020) and uphold the foundational pillars of the nlp ecosystem @xcite . ## conclusion chain-of-thought is an outstanding prompting technique. however, the imbalance of languages in pre-training data does not always produce robust results. different state-of-the-art works have proposed cross-lingual techniques to align performances obtained in different languages. they are limited to handling one language at a time or proposing multiple prompting stages, making them difficult to manage. in this paper, we propose cross-tot, a prompting technique to elicit multistep reasoning abilities in cross-lingual scenarios. hence, we elicit models to deliver answers in a selfconsistent way, collaborating to the final answer. we have shown the functionality of our cross-tot through performance improvements obtained in a multilingual mathematical problem task. in addition, we have demonstrated the scalability in tasks related to commonsense reasoning and language understanding. finally, we conducted a series of in-depth analyses in which we measured the impact brought about by low-resource vs. high-resource languages and the inclusion of english. our contribution aims to propose more robust models that can break down issues arising from language barriers and provide more reliable results.