Fine-tuning Large Language Models for Adaptive Machine Translation
Yasmin Moslem Rejwanul Haque Andy Way
This paper presents the outcomes of fine-tuning Mistral 7B, a general-purpose
large language model (LLM), for adaptive machine translation (MT). The
fine-tuning process involves utilising a combination of zero-shot and one-shot
translation prompts within the medical domain. The primary objective is to
enhance real-time adaptive MT capabilities of Mistral 7B, enabling it to adapt
translations to the required domain at inference time. The results,
particularly for Spanish-to-English MT, showcase the efficacy of the fine-tuned
model, demonstrating quality improvements in both zero-shot and one-shot
translation scenarios, surpassing Mistral 7B's baseline performance. Notably,
the fine-tuned Mistral outperforms ChatGPT "gpt-3.5-turbo" in zero-shot
translation while achieving comparable one-shot translation quality. Moreover,
the zero-shot translation of the fine-tuned Mistral matches NLLB 3.3B's
performance, and its one-shot translation quality surpasses that of NLLB 3.3B.
These findings emphasise the significance of fine-tuning efficient LLMs like
Mistral 7B to yield high-quality zero-shot translations comparable to
task-oriented models like NLLB 3.3B. Additionally, the adaptive gains achieved
in one-shot translation are comparable to those of commercial LLMs such as
ChatGPT. Our experiments demonstrate that, with a relatively small dataset of
20,000 segments that incorporate a mix of zero-shot and one-shot prompts,
fine-tuning significantly enhances Mistral's in-context learning ability,
especially for real-time adaptive MT.