The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities
David Stap, Eva Hasler, Bill Byrne, Christof Monz, Ke M. Tran · 2024
Fine-tuning large language models (LLMs) for machine translation has shown improvements in overall translation quality.However, it is unclear what is the impact of fine-tuning on desirable LLM behaviors that are not present in neural machine translation models, such as steerability, inherent document-level translation abilities, and the ability to produce less literal translations.We perform an extensive translation evaluation on the LLaMA and Falcon family of models with model size ranging from 7 billion up to 65 billion parameters.Our results show that while fine-tuning improves the general translation quality of LLMs, several abilities degrade.In particular, we observe a decline in the ability to perform formality steering, to produce technical translations through few-shot examples, and to perform documentlevel translation.On the other hand, we observe that the model produces less literal translations after fine-tuning on parallel data.We show that by including monolingual data as part of the fine-tuning data we can maintain the abilities while simultaneously enhancing overall translation quality.Our findings emphasize the need for fine-tuning strategies that preserve the benefits of LLMs for machine translation.