# Refining Translations with LLMs: A Constraint-Aware Iterative Prompting Approach

arXiv: https://arxiv.org/abs/2411.08348 · HTML: https://mlsanity.com/paper/2411.08348/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 14 of 14)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Don't Make Your LLM an Evaluation Benchmark Cheater](https://mlsanity.com/paper/2311.01964/) (2023)
- [Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation](https://mlsanity.com/paper/2302.07856/) (2023)
- [Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning](https://mlsanity.com/paper/2406.07081/) (2024)
- [Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences](https://mlsanity.com/paper/2007.02671/) (2020)
- [Bridging Neural Machine Translation and Bilingual Dictionaries](https://mlsanity.com/paper/1610.07272/) (2016)
- [Chain-of-Dictionary Prompting Elicits Translation in Large Language Models](https://mlsanity.com/paper/2305.06575/) (2023)
- [Bilingual Dictionary-based Language Model Pretraining for Neural Machine Translation](https://mlsanity.com/paper/2103.07040/) (2021)

## Built on by (most cited first, 2 of 2)

- [Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement](https://mlsanity.com/paper/2504.05614/) (2025)
- [Exposing the Cracks: Vulnerabilities of Retrieval-Augmented LLM-based Machine Translation](https://mlsanity.com/paper/2510.00829/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have demonstrated remarkable proficiency in machine translation (MT), even without specific training on the languages in question. However, translating rare words in low-resource or domain-specific contexts remains challenging for LLMs. To address this issue, we propose a multi-step prompt chain that enhances translation faithfulness by prioritizing key terms crucial for semantic accuracy. Our method first identifies these keywords and retrieves their translations from a bilingual dictionary, integrating them into the LLM's context using Retrieval-Augmented Generation (RAG). We further mitigate potential output hallucinations caused by long prompts through an iterative self-checking mechanism, where the LLM refines its translations based on lexical and semantic constraints. Experiments using Llama and Qwen as base models on the FLORES-200 and WMT datasets demonstrate significant improvements over baselines, highlighting the effectiveness of our approach in enhancing translation faithfulness and robustness, particularly in low-resource scenarios.