Aligning Cross-Lingual Chain-of-Thought Reasoning Across Languages

Core Concepts

Large Language Models can deliver cross-lingual multi-step reasoning by aligning parallel reasoning paths across languages through a self-consistent prompting mechanism.

Abstract

The paper proposes a novel method called Cross-lingual Tree-of-Thoughts (Cross-ToT) to enable Large Language Models (LLMs) to perform multi-step reasoning across different languages. The key insights are: Cross-ToT elicits the LLM to generate parallel reasoning paths in different languages that converge to a final solution through a self-consistent prompting mechanism. The different reasoning paths interact and refine each other during the intermediate steps, leading to a self-correcting and self-consistent final answer. Experiments on arithmetic reasoning, language understanding, and commonsense reasoning tasks show that Cross-ToT significantly outperforms existing cross-lingual prompting techniques. Further analysis reveals that the inclusion of English in the prompting plays a beneficial role in improving the overall cross-lingual performance, especially for low-resource languages. The quality of the reasoning paths generated by Cross-ToT is also found to be superior, with higher faithfulness, informativeness, and consistency compared to previous approaches.

Stats

"Leah has 32 chocolates and her sister has 42. If they ate 35 pieces, how many pieces do they have left?" "Premise: Leah has 32 chocolates and her sister has 42. Hypothesis: They ate 35 pieces."

Quotes

"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." "Experimental results reveal that our method, based on a single prompt, outperforms the baselines and achieves the SOTA performance on different languages in different tasks."

Key Insights Distilled From

Empowering Multi-step Reasoning across Languages via Tree-of-Thoughts

by Leonardo Ran... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2311.08097.pdf

Deeper Inquiries

How can the Cross-ToT approach be extended to handle more diverse language families and scripts?

The Cross-ToT approach can be extended to handle more diverse language families and scripts by incorporating a wider range of languages and scripts into the prompting mechanism. This can be achieved by expanding the dataset used for pre-training to include languages from various language families and scripts. Additionally, the prompts can be tailored to accommodate the linguistic characteristics and structures of different language families and scripts. By incorporating a diverse set of languages and scripts, the Cross-ToT approach can enhance its cross-lingual reasoning capabilities and cater to a more extensive range of linguistic diversity.

What are the potential limitations of the self-consistent reasoning mechanism, and how can it be further improved?

One potential limitation of the self-consistent reasoning mechanism is the risk of converging on incorrect reasoning paths if the initial reasoning steps are flawed. To address this limitation, the mechanism can be further improved by incorporating feedback loops that allow for the correction of erroneous reasoning paths. By introducing mechanisms for self-correction and validation of reasoning steps, the model can iteratively refine its reasoning process and converge on more accurate solutions. Additionally, incorporating a diversity of perspectives and reasoning strategies in the collaborative reasoning process can help mitigate the risk of bias or incorrect reasoning paths.

How can the insights from Cross-ToT be applied to enhance the cross-lingual capabilities of smaller language models?

The insights from Cross-ToT can be applied to enhance the cross-lingual capabilities of smaller language models by leveraging the collaborative reasoning approach and self-consistent reasoning mechanisms. Smaller language models can benefit from the multi-step reasoning paths generated by Cross-ToT, which can provide a structured framework for tackling complex tasks in a cross-lingual context. By incorporating self-consistent reasoning mechanisms, smaller language models can improve their ability to generate accurate and contextually relevant responses across different languages. Additionally, the collaborative nature of the reasoning process can help smaller language models leverage diverse perspectives and reasoning strategies to enhance their cross-lingual capabilities.