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

• URL: http://arxiv.org/abs/2411.08348v1
• Date: Wed, 13 Nov 2024 05:40:24 GMT
• Title: Refining Translations with LLMs: A Constraint-Aware Iterative Prompting Approach
• Authors: Shangfeng Chen, Xiayang Shi, Pu Li, Yinlin Li, Jingjing Liu,
• Abstract summary: Large language models (LLMs) have demonstrated remarkable proficiency in machine translation (MT) We propose a multi-step prompt chain that enhances translation faithfulness by prioritizing key terms crucial for semantic accuracy. Experiments using Llama and Qwen as base models on the FLORES-200 and WMT datasets demonstrate significant improvements over baselines.
• Score: 7.5069214839655345
• License:
• Abstract: 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.

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