  Large language models (LLMs) have made significant progress in machine
translation (MT). However, their potential in domain-specific MT remains
under-explored. Current LLM-based MT systems still face several challenges.
First, for LLMs with in-context learning, their effectiveness is highly
sensitive to input translation examples, and processing them can increase
inference costs. They often require extra post-processing due to
over-generation. Second, LLMs with fine-tuning on domain-specific data often
require high training costs for domain adaptation, and may weaken the zero-shot
MT capabilities of LLMs due to over-specialization. The aforementioned methods
can struggle to translate rare words in domain transfer scenarios. To address
these challenges, this paper proposes a prompt-oriented fine-tuning method,
denoted as LlamaIT, to effectively and efficiently fine-tune a general-purpose
LLM for domain-specific MT tasks. First, we construct a task-specific
mix-domain dataset, which is then used to fine-tune the LLM with LoRA. This can
eliminate the need for input translation examples, post-processing, or
over-specialization. By zero-shot prompting with instructions, we adapt the MT
tasks to the target domain at inference time. To further elicit the MT
capability for rare words, we construct new prompts by incorporating
domain-specific bilingual vocabulary. We also conduct extensive experiments on
both publicly available and self-constructed datasets. The results show that
our LlamaIT can significantly enhance the domain-specific MT capabilities of
the LLM, meanwhile preserving its zero-shot MT capabilities.
