The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities

• URL: http://arxiv.org/abs/2405.20089v2
• Date: Tue, 6 Aug 2024 14:46:29 GMT
• Title: The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities
• Authors: David Stap, Eva Hasler, Bill Byrne, Christof Monz, Ke Tran,
• Abstract summary: Fine-tuning large language models (LLMs) for machine translation has shown improvements in overall translation quality. 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. We observe a decline in the ability to perform formality steering, to produce technical translations through few-shot examples, and to perform document-level translation.
• Score: 18.175795328685986
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 document-level 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.

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