[Translation-CoT: A Human-Inspired Chain-of-Thought Framework for Multilingual LLM Translation](https://aclanthology.org/2026.amta-research.2.pdf)
[Tabia Tanzin Prama](/people/tabia-tanzin-prama/),
[Juniper L Lovato](/people/juniper-l-lovato/unverified/),
[Chris Danforth](/people/chris-danforth/unverified/),
[Peter Dodds](/people/peter-dodds/)
Abstract
Large language models (LLMs) have transformed machine translation, yet mistranslations, hallucinations, and unnatural phrasing still limit their effectiveness, particularly for low-resource languages. We propose Translation-CoT, a chain-of-thought prompting strategy that breaks translation into structured stages (lexical retrieval, grammatical analysis, and topic identification), followed by a refinement step to improve fluency, tone, and idiomatic expression. We evaluate Translation-CoT across 14 languages from 14 language families and multiple LLMs (GPT-4o, GPT-4o-mini, LLaMA 3.1, and Gemma 2), with GPT-4o performing best overall, in both English ↔ non-English (X) translation settings. Compared with zero-shot prompting, in-context learning, and existing chain-of-thought prompting methods (Tree-of-Thought (ToT) and Learning-Oriented Prompting (LOT)), Translation-CoT outperforms these prompting strategies on multilingual machine translation across BLEU, ChrF, and METEOR, with especially strong gains in the more difficult English→non-English (X) setting and in low-resource languages. Human evaluation further shows higher preference scores and lower MQM penalty scores, indicating fewer mistranslations, omissions, awkward phrasing, and hallucinations with Translation-CoT. Overall, our results show that structured, task-aware prompting is an effective approach for improving multilingual translation quality and robustness in LLMs.- Anthology ID:
- 2026.amta-research.2
- Volume:
[Proceedings of the 17th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)](/volumes/2026.amta-research/)- Month:
- August
- Year:
- 2026
- Address:
- Québec City, Canada
- Editors:
[Eleftheria Briakou](/people/eleftheria-briakou/unverified/),[Jeremy Gwinnup](/people/jeremy-gwinnup/),[Shivali Goel](/people/shivali-goel/unverified/)- Venue:
[AMTA](/venues/amta/)- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 3–27
- Language:
- URL:
[https://aclanthology.org/2026.amta-research.2/](https://aclanthology.org/2026.amta-research.2/)- DOI:
- Cite (ACL):
- Tabia Tanzin Prama, Juniper L Lovato, Chris Danforth, and Peter Dodds. 2026. Translation-CoT: A Human-Inspired Chain-of-Thought Framework for Multilingual LLM Translation. InProceedings of the 17th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), pages 3–27, Québec City, Canada. Association for Machine Translation in the Americas. - Cite (Informal):
[Translation-CoT: A Human-Inspired Chain-of-Thought Framework for Multilingual LLM Translation](https://aclanthology.org/2026.amta-research.2/)(Prama et al., AMTA 2026)- PDF:
[https://aclanthology.org/2026.amta-research.2.pdf](https://aclanthology.org/2026.amta-research.2.pdf)