Reflective Translation: Improving Low-Resource Machine Translation via Structured Self-Reflection
Abstract
Low-resource languages such as isiZulu and isiXhosa face persistent challenges in machine translation due to limited parallel data and linguistic resources. Recent advances in large language models suggest that self-reflection, prompting a model to critique and revise its own outputs, can improve reasoning quality and factual consistency. Building on this idea, this paper introduces Reflective Translation, a prompt-based framework in which a model generates an initial translation, produces a structured self-critique, and then uses this reflection to generate a refined translation. The approach is evaluated on English-isiZulu and English-isiXhosa translation using OPUS-100 and NTREX-African, across multiple prompting strategies and confidence thresholds. Results show consistent improvements in both BLEU and COMET scores between first- and second-pass translations, with average gains of up to +0.22 BLEU and +0.18 COMET. Statistical significance testing using paired nonparametric tests confirms that these improvements are robust. The proposed method is model-agnostic, requires no fine-tuning, and introduces a reflection-augmented dataset that can support future supervised or analysis-driven work. These findings demonstrate that structured self-reflection is a practical and effective mechanism for improving translation quality in low-resource settings.
Cite
@article{arxiv.2601.19871,
title = {Reflective Translation: Improving Low-Resource Machine Translation via Structured Self-Reflection},
author = {Nicholas Cheng},
journal= {arXiv preprint arXiv:2601.19871},
year = {2026}
}
Comments
12 pages, 3 figures, 6 tables. Accepted to the NeurIPS 2025 Workshop on Multilingual Representation Learning (Mexico City) and the AAAI 2025 Workshop on Language Models for Under-Resourced Communities (LM4UC). Code and data available at: https://github.com/Nickcheng123/reflective-translation-mt