# Iterative Translation Refinement with Large Language Models

arXiv: https://arxiv.org/abs/2306.03856 · HTML: https://mlsanity.com/paper/2306.03856/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 66 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Large Language Models Are State-of-the-Art Evaluators of Translation Quality](https://mlsanity.com/paper/2302.14520/) (2023)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [Levenshtein Transformer](https://mlsanity.com/paper/1905.11006/) (2019)
- [Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement](https://mlsanity.com/paper/1802.06901/) (2018)
- [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023)
- [In-context Examples Selection for Machine Translation](https://mlsanity.com/paper/2212.02437/) (2022)
- [Prompting PaLM for Translation: Assessing Strategies and Performance](https://mlsanity.com/paper/2211.09102/) (2022)
- [BLEU might be Guilty but References are not Innocent](https://mlsanity.com/paper/2004.06063/) (2020)
- [Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models](https://mlsanity.com/paper/2303.13809/) (2023)
- [Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability](https://mlsanity.com/paper/2305.10266/) (2023)
- [Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation](https://mlsanity.com/paper/1808.10432/) (2018)
- [Facebook AI WMT21 News Translation Task Submission](https://mlsanity.com/paper/2108.03265/) (2021)
- [Leveraging GPT-4 for Automatic Translation Post-Editing](https://mlsanity.com/paper/2305.14878/) (2023)
- [Translationese as a Language in "Multilingual" NMT](https://mlsanity.com/paper/1911.03823/) (2019)
- [Do GPTs Produce Less Literal Translations?](https://mlsanity.com/paper/2305.16806/) (2023)
- [Post-editese: an Exacerbated Translationese](https://mlsanity.com/paper/1907.00900/) (2019)
- [EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints](https://mlsanity.com/paper/2011.06868/) (2020)
- [Can Automatic Post-Editing Improve NMT?](https://mlsanity.com/paper/2009.14395/) (2020)
- [Human-Paraphrased References Improve Neural Machine Translation](https://mlsanity.com/paper/2010.10245/) (2020)

## Built on by (most cited first, 27 of 27)

- [When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs](https://mlsanity.com/paper/2406.01297/) (2024)
- [Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies](https://mlsanity.com/paper/2308.03188/) (2023)
- [A Survey on Self-Evolution of Large Language Models](https://mlsanity.com/paper/2404.14387/) (2024)
- [A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning](https://mlsanity.com/paper/2311.07954/) (2023)
- [SuperCorrect: Advancing Small LLM Reasoning with Thought Template Distillation and Self-Correction](https://mlsanity.com/paper/2410.09008/) (2024)
- [A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models](https://mlsanity.com/paper/2508.08712/) (2025)
- [LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought](https://mlsanity.com/paper/2405.06705/) (2024)
- [Language Imbalance Driven Rewarding for Multilingual Self-improving](https://mlsanity.com/paper/2410.08964/) (2024)
- [An Empirical Study of Translation Hypothesis Ensembling with Large Language Models](https://mlsanity.com/paper/2310.11430/) (2023)
- [On the Intrinsic Self-Correction Capability of LLMs: Uncertainty and Latent Concept](https://mlsanity.com/paper/2406.02378/) (2024)
- [Enhancing LLM Reasoning with Multi-Path Collaborative Reactive and Reflection agents](https://mlsanity.com/paper/2501.00430/) (2024)
- [Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translation](https://mlsanity.com/paper/2503.04554/) (2025)
- [StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving](https://mlsanity.com/paper/2311.08803/) (2023)
- [Feedback Friction: LLMs Struggle to Fully Incorporate External Feedback](https://mlsanity.com/paper/2506.11930/) (2025)
- [Towards Effective Disambiguation for Machine Translation with Large Language Models](https://mlsanity.com/paper/2309.11668/) (2023)
- [Terminology-Aware Translation with Constrained Decoding and Large Language Model Prompting](https://mlsanity.com/paper/2310.05824/) (2023)
- [Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models](https://mlsanity.com/paper/2412.13791/) (2024)
- [Autoregressive Models Rival Diffusion Models at ANY-ORDER Generation](https://mlsanity.com/paper/2601.13228/) (2026)
- [Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models](https://mlsanity.com/paper/2309.10444/) (2023)
- [SCALE: Synergized Collaboration of Asymmetric Language Translation Engines](https://mlsanity.com/paper/2309.17061/) (2023)
- [PrefixLLM: LLM-aided Prefix Circuit Design](https://mlsanity.com/paper/2412.02594/) (2024)
- [Agents of Discovery](https://mlsanity.com/paper/2509.08535/) (2025)
- [Towards Self-Refinement of Vision-Language Models with Triangular Consistency](https://mlsanity.com/paper/2510.10487/) (2025)
- [Speech Translation Refinement using Large Language Models](https://mlsanity.com/paper/2501.15090/) (2025)
- [Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction](https://mlsanity.com/paper/2408.04775/) (2024)
- [Unleashing the True Potential of LLMs: A Feedback-Triggered Self-Correction with Long-Term Multipath Decoding](https://mlsanity.com/paper/2509.07676/) (2025)
- [Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning](https://mlsanity.com/paper/2607.19181/) (2026)

## Abstract (arXiv)

We propose iteratively prompting a large language model to self-correct a translation, with inspiration from their strong language understanding and translation capability as well as a human-like translation approach. Interestingly, multi-turn querying reduces the output's string-based metric scores, but neural metrics suggest comparable or improved quality. Human evaluations indicate better fluency and naturalness compared to initial translations and even human references, all while maintaining quality. Ablation studies underscore the importance of anchoring the refinement to the source and a reasonable seed translation for quality considerations. We also discuss the challenges in evaluation and relation to human performance and translationese.