Large Language Models (LLM's) have demonstrated considerable success in
various Natural Language Processing tasks, but they have yet to attain
state-of-the-art performance in Neural Machine Translation (NMT). Nevertheless,
their significant performance in tasks demanding a broad understanding and
contextual processing shows their potential for translation. To exploit these
abilities, we investigate using LLM's for MT and explore recent
parameter-efficient fine-tuning techniques. Surprisingly, our initial
experiments find that fine-tuning for translation purposes even led to
performance degradation. To overcome this, we propose an alternative approach:
adapting LLM's as Automatic Post-Editors (APE) rather than direct translators.
Building on the LLM's exceptional ability to process and generate lengthy
sequences, we also propose extending our approach to document-level
translation. We show that leveraging Low-Rank-Adapter fine-tuning for APE can
yield significant improvements across both sentence and document-level metrics
while generalizing to out-of-domain data. Most notably, we achieve a
state-of-the-art accuracy rate of 89\% on the ContraPro test set, which
specifically assesses the model's ability to resolve pronoun ambiguities when
translating from English to German. Lastly, we investigate a practical scenario
involving manual post-editing for document-level translation, where reference
context is made available. Here, we demonstrate that leveraging human
corrections can significantly reduce the number of edits required for
subsequent translations\footnote{Interactive Demo for integrating manual
feedback can be found
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利用大型语言模型（LLM）进行神经机器翻译和自动后编辑，并借鉴低秩适配器微调的方法，在句子和文档级别的度量上取得显著改进，同时还研究了利用人工修正来减少后续翻译所需的编辑次数的实际应用场景。