Incorporating Lexicon-Aligned Prompting in Large Language Model for Tangut–Chinese Translation

AbstractThis paper proposes a machine translation approach for Tangut–Chinese using a large language model (LLM) enhanced with lexical knowledge. We fine-tune a Qwen-based LLM using Tangut–Chinese parallel corpora and dictionary definitions. Experimental results demonstrate that incorporating single-character dictionary definitions leads to the best BLEU-4 score of 72.33 for literal translation. Additionally, applying a chain-of-thought prompting strategy significantly boosts free translation performance to 64.20. The model also exhibits strong few-shot learning abilities, with performance improving as the training dataset size increases. Our approach effectively translates both simple and complex Tangut sentences, offering a robust solution for low-resource language translation and contributing to the digital preservation of Tangut texts.