Improve Fluency Of Neural Machine Translation Using Large Language Models (2025)

Abstract: Large language models (LLMs) demonstrate significant capabilities in many natural language processing. However, their performance in machine translation is still behind the models that are specially trained for machine translation with an encoder-decoder architecture. This paper investigates how to improve neural machine translation (NMT) with LLMs. Our proposal is based on an empirical insight that NMT gets worse fluency than human translation. We propose to use LLMs to enhance the fluency of NMT’s generation by integrating a language model at the target side. we use contrastive learning to constrain fluency so that it does not exceed the LLMs. Our experiments on three language pairs show that this method can improve the performance of NMT. Our empirical analysis further demonstrates that this method improves the fluency at the target side. Our experiments also show that some straightforward post-processing methods using LLMs, such as re-ranking and refinement, are not effective.

## introduction large language models (llms) such as gpt @xcite and llama @xcite have demonstrated significant capabilities in various domains, including language understanding and generation tasks @xcite . however, the evaluations @xcite show that llms' performance in machine translation is still behind the models dedicated to the task. these dedicated models often use an encoder-decoder architecture and are trained with parallel corpora. this raises a question: can llms still help improve neural machine translation (nmt)? a key translation challenge is the balance between adequacy and fluency. according to @xcite , nmt is good at adequacy and weak at fluency compared to human translation. there are some post-processing methods to use llms on nmt's outputs to improve fluency. we can follow the reranking methods in nmt @xcite @xcite . llms can be used to rerank the candidates that are output from nmt, and the one with the smallest perplexity, according to llm's evaluation, is chosen as the final output. alternatively, we apply the self-refine method in llm @xcite @xcite to nmt's outputs. the translations from nmt are included in the prompt and an llm is explicitly asked to refine their fluency. these two methods are used as baselines in our experiments. results show that they cannot consistently improve the performance of nmt. we propose to improve the fluency of nmt's translation by integrating the language capability of llms during training the nmt model. we use a two-pass strategy in the decoder. the first pass is a normal one using parallel sentences. the second pass only uses the target sentences in the training data. the objective is to train a target language model while training the translation model. this is realized by assigning all ones to the context vectors from the encoder for the second pass. furthermore, we use an llm to infer the training set and get their negative log-likelihoods. these data are used with contrastive learning to constraint the fluency of the target language model not to exceed the llm's. we conduct experiments on three language pairs: german-english (de-en), russian-english (ru-en), and french-english (fr-en). the results show that our method effectively improves the performance of nmt. our empirical analysis further demonstrates that our method improves fluency on the target side, and contrastive learning with knowledge from the llm plays an important role in achieving gains. ## conclusion this paper investigates how to improve neural machine translation (nmt) with large language models (llms). our experiments show that post-processing methods like re-ranking and selfrefining are not effective. based on the insight that nmt is good at adequacy and weak at fluency, we propose to use llms to enhance the fluency of nmt's generation by integrating a language model at the target side and using contrastive learning to constraint the probabilities to a ceiling, the llm's fluency. our experiments on three language pairs (de-en, ru-en, and fr-en) show that this method effectively improves the performance of nmt. the empirical analysis further demonstrates that this method improves the fluency at the target side and contrastive learning with knowledge from the llm plays an important role in achieving the gains.