Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning
Duarte Alves, Nuno Guerreiro, João Alves, José P. Pombal, Ricardo Rei, José G. C. de Souza, Pierre Colombo, André F. T. Martins · 2023
Large language models (LLMs) are a promising avenue for machine translation (MT).However, current LLM-based MT systems are brittle: their effectiveness highly depends on the choice of few-shot examples and they often require extra post-processing due to overgeneration.Alternatives such as finetuning on translation instructions are computationally expensive and may weaken in-context learning capabilities, due to overspecialization.In this paper, we provide a closer look at this problem.We start by showing that adapter-based finetuning with LoRA matches the performance of traditional finetuning while reducing the number of training parameters by a factor of 50.This method also outperforms few-shot prompting and eliminates the need for post-processing or in-context examples.However, we show that finetuning generally degrades few-shot performance, hindering adaptation capabilities.Finally, to obtain the best of both worlds, we propose a simple approach that incorporates few-shot examples during finetuning.Experiments on 10 language pairs show that our proposed approach recovers the original few-shot capabilities while keeping the added benefits of finetuning.