B rid G MT : Enhancing LLM s’ Machine Translation Capabilities with Sentence Bridging and Gradual MT (2025)

Abstract: Recent Large Language Models (LLMs) have demonstrated impressive translation performance without requiring fine-tuning on additional parallel corpora. However, they still face significant challenges in certain scenarios, particularly when translating low-resource languages. A common approach to address this issue is to provide external knowledge, such as few-shot examples, to assist LLMs in translating specific source sentences. However, this method is fundamentally limited by the quality or quantity of relevant sources, which cannot always be guaranteed. To reduce LLMs’ reliance on external sources, we propose BridG MT, a method that combines Sentence Bridging, which generates a sequence of sentences as a bridge that gradually transition from easy-to-translate to more difficult, and Gradual MT, which sequentially translates these sentences using earlier translations as few-shot examples for subsequent ones. Experiments conducted on four LLMs across seven languages demonstrate that our method effectively enhances translation performance, even outperforming translation methods that rely on a large number of few-shot examples.

## introduction recent large language models (llms) have shown strong performance in translation tasks without the need for fine-tuning on specific parallel datasets. previous studies have demonstrated that llms' translation capabilities are reliable in most use cases, particularly when the source and target language are high-resource languages @xcite @xcite . however, because llms require training on large corpora, they still face challenges when translating low-resource languages that are not sufficiently represented in the training corpora. @xcite @xcite . previous research has attempted to address these challenges by leveraging the in-context-learning capabilities of large language models (llms), particularly through the use of external knowledge such as few-shot examples or dictionaries during inference. however, relevant examples are not always guaranteed to be available, and constructing such external knowledge sources can be costly. a potential solution is to reduce reliance on external sources altogether. in this paper, we investigate whether llms' translation capabilities can be improved without relying on external knowledge. our approach is grounded in a fundamental idea: machine translation can be understood as a mapping between two (sub)spaces representing the source and target languages. within the source language space, some regions yield accurate translations, while others do not. the key question is whether we can expand the model's confident region by refeeding examples from it, ultimately extending into weaker regions and improving performance in those areas. we propose bridg mt to enhance machine translation by bridging regions where the llm performs well with those where it struggles. bridg mt integrates two key techniques: sentence bridging and gradual mt. sentence bridging is a prompting method that generates a sequence of sentences, progressively transitioning between them. gradual mt iteratively translates a list of sentences, using the model's previous translations as few-shot examples for subsequent ones. sentence bridging helps bridge high-and low-performance regions, while gradual mt gradually expands the model's strong performance areas by leveraging in-context learning. these concepts are visually illustrated in figure 1 . we evaluate the effectiveness of bridg mt by applying it to four different llms: gpt-3.5, mistral-nemo-instruct, llama-3.1-70b-instruct, and llama-3.1-8b-instruct. the experiments cover seven target languages: german (de), chinese (zh), hindi (hi), korean (ko), swahili (sw), marathi (mr), and bengali (bn). the results demonstrate that bridg mt significantly enhances the translation capabilities of llms, particularly in lowresource languages. to the best of our knowledge, we are the first to propose the concept of sentence bridging. ## methodology we introduce two major components of bridg mt-sentence bridging and gradual mt-in sections 3.1 and 3.2, respectively, and then describe how we combine them in section 3.3. ## results we first conducted a series of ablation experiments for en-ko translation using gpt 3.5 to identify the optimal configuration, including start selection ## ablation study to find the optimal configuration for the bridg mt, we conducted an ablation study on en-ko transla-tion task using gpt-3.5 in terms of four different dimensions: start sentence selection criteria, number of start sentences, mt result aggregation strategy, and filtering strategy. we utilized xcomet with gold translations in the ablation study to construct the highest-quality start pool possible. full results with all different combination of ablation settings can be found in appendix g. ## conclusion in this paper, we proposed bridg mt, a novel method to enhance the machine translation capabilities of various llms. bridg mt leverages sentence bridging and gradual mt to guide models, eliciting stronger translation performance. experimental results across various models and languages demonstrate that our approach consistently improves translation quality, particularly in lowresource languages. our approach is practical in that it does not require extra training and does not conflict with previous methods that utilize other kinds of prompting techniques.