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

TL;DR

This paper explores finetuning large language models for machine translation, demonstrating that adapter-based methods can outperform few-shot prompting, and proposes a hybrid approach to retain few-shot capabilities while benefiting from finetuning.

Contribution

It introduces an adapter-based finetuning method that matches traditional finetuning performance with fewer parameters and proposes a hybrid approach to preserve few-shot learning abilities.

Findings

Adapter-based finetuning matches traditional finetuning performance.

Finetuning can degrade few-shot capabilities.

Hybrid approach recovers few-shot performance while benefiting from finetuning.

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.