3 papers
cs.CL2024
The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities
David Stap, Eva Hasler, Bill Byrne +2
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…
cs.CL2024
A Preference-driven Paradigm for Enhanced Translation with Large Language Models
Dawei Zhu, Sony Trenous, Xiaoyu Shen +3
Recent research has shown that large language models (LLMs) can achieve remarkable translation performance through supervised fine-tuning (SFT) using only a small amount of paralle…
cs.CL2023
Trained MT Metrics Learn to Cope with Machine-translated References
Jannis Vamvas, Tobias Domhan, Sony Trenous +2
Neural metrics trained on human evaluations of MT tend to correlate well with human judgments, but their behavior is not fully understood. In this paper, we perform a controlled ex…