  General-purpose Large Language Models (LLMs) like GPT-4 have achieved
remarkable advancements in machine translation (MT) by leveraging extensive web
content. On the other hand, translation-specific LLMs are built by pre-training
on domain-specific monolingual corpora and fine-tuning with human-annotated
translation data. Despite the superior performance, these methods either demand
an unprecedented scale of computing and data or substantial human editing and
annotation efforts. In this paper, we develop Ladder, a novel model-agnostic
and cost-effective tool to refine the performance of general LLMs for MT.
Ladder is trained on pseudo-refinement triplets which can be easily obtained
from existing LLMs without additional human cost. During training, we propose a
hierarchical fine-tuning strategy with an easy-to-hard schema, improving
Ladder's refining performance progressively. The trained Ladder can be
seamlessly integrated with any general-purpose LLMs to boost their translation
performance. By utilizing Gemma-2B/7B as the backbone, Ladder-2B can elevate
raw translations to the level of top-tier open-source models (e.g., refining
BigTranslate-13B with +6.91 BLEU and +3.52 COMET for XX-En), and Ladder-7B can
further enhance model performance to be on par with the state-of-the-art GPT-4.
Extensive ablation and analysis corroborate the effectiveness of Ladder in
diverse settings. Our code is available at https://github.com/fzp0424/Ladder
