  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.
