Large language models (LLMs) exhibit outstanding performance in machine translation via in-context learning. In contrast to sentence-level translation, document-level translation (DOCMT) by LLMs based on in-context learning faces two major challenges: firstly, document translations generated by LLMs are often incoherent; secondly, the length of demonstration for in-context learning is usually limited. To address these issues, we propose a Context-Aware Prompting method (CAP), which enables LLMs to generate more accurate, cohesive, and coherent translations via in-context learning. CAP takes into account multi-level attention, selects the most relevant sentences to the current one as context, and then generates a summary from these collected sentences. Subsequently, sentences most similar to the summary are retrieved from the datastore as demonstrations, which effectively guide LLMs in generating cohesive and coherent translations. We conduct extensive experiments across various DOCMT tasks, and the results demonstrate the effectiveness of our approach, particularly in zero pronoun translation (ZPT) and literary translation tasks.

Using large language models (LLMs) for machine translation via in-context learning (ICL) has become an interesting research direction of machine translation (MT) in recent years. Its main idea is to retrieve a few translation pairs as demonstrations from an additional datastore (parallel corpus) to guide translation without updating the LLMs. However, the underlying noise of retrieved demonstrations usually dramatically deteriorate the performance of LLMs. In this paper, we propose a robust method to enable LLMs to achieve robust translation with ICL. The method incorporates a multi-view approach, considering both sentence- and word-level information, to select demonstrations that effectively avoid noise. At the sentence level, a margin-based score is designed to avoid semantic noise. At the word level, word embeddings are utilized to evaluate the related tokens and change the weight of words in demonstrations. By considering both sentence- and word-level similarity, the proposed method provides fine-grained demonstrations that effectively prompt the translation of LLMs. Experimental results demonstrate the effectiveness of our method, particularly in domain adaptation.