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    <abstract>As Large Language Models (LLMs) continue to advance in capability, prompt engineering has emerged as a crucial method for optimizing their performance on specialized tasks. While prompting strategies like Zero-shot, Few-shot, Chain-of-Thought, and Tree-of-Thought have demonstrated significant improvements in reasoning tasks, their application to machine translation has received comparatively less attention. This paper systematically evaluates these prompting techniques across diverse language pairs and domains, measuring their effect on translation quality. Our findings reveal substantial performance variations between prompting methods, with certain strategies offering consistent improvements for specific language directions and complexity levels. These results provide valuable insights for developing more effective LLM-based translation systems without requiring model fine-tuning and complement existing works in the field.</abstract>
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%0 Conference Proceedings
%T Reasoning for Translation: Comparative Analysis of Chain-of-Thought and Tree-of-Thought Prompting for LLM Translation
%A Nguyen, Lam
%A Xu, Yang
%Y Zhao, Jin
%Y Wang, Mingyang
%Y Liu, Zhu
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-254-1
%F nguyen-xu-2025-reasoning
%X As Large Language Models (LLMs) continue to advance in capability, prompt engineering has emerged as a crucial method for optimizing their performance on specialized tasks. While prompting strategies like Zero-shot, Few-shot, Chain-of-Thought, and Tree-of-Thought have demonstrated significant improvements in reasoning tasks, their application to machine translation has received comparatively less attention. This paper systematically evaluates these prompting techniques across diverse language pairs and domains, measuring their effect on translation quality. Our findings reveal substantial performance variations between prompting methods, with certain strategies offering consistent improvements for specific language directions and complexity levels. These results provide valuable insights for developing more effective LLM-based translation systems without requiring model fine-tuning and complement existing works in the field.
%R 10.18653/v1/2025.acl-srw.17
%U https://aclanthology.org/2025.acl-srw.17/
%U https://doi.org/10.18653/v1/2025.acl-srw.17
%P 259-275

Markdown (Informal)

[Reasoning for Translation: Comparative Analysis of Chain-of-Thought and Tree-of-Thought Prompting for LLM Translation](https://aclanthology.org/2025.acl-srw.17/) (Nguyen & Xu, ACL 2025)

• Reasoning for Translation: Comparative Analysis of Chain-of-Thought and Tree-of-Thought Prompting for LLM Translation (Nguyen & Xu, ACL 2025)

ACL

• Lam Nguyen and Yang Xu. 2025. Reasoning for Translation: Comparative Analysis of Chain-of-Thought and Tree-of-Thought Prompting for LLM Translation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 259–275, Vienna, Austria. Association for Computational Linguistics.