Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning

Duarte Miguel Alves, Nuno M Guerreiro, João Alves, José Pombal, Ricardo Rei, José G. C. de Souza, Pierre Colombo, Andre Martins

Submission Type: Regular Short Paper

Submission Track: Machine Translation

Submission Track 2: Language Modeling and Analysis of Language Models

Keywords: Machine Translation; In-Context Learning; Efficient Finetuning

TL;DR: We explore finetuning and in-context learning for adapting large language models for machine translation.

Abstract: 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.

Submission Number: 5491