4 papers
What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation
Shaomu Tan, Dawei Zhu, Ke Tran +5
Iterative self-refinement is a simple inference-time strategy for machine translation: an LLM revises its own translation over multiple inference-time passes. Yet document-scale re…
Remedy-R: Generative Reasoning for Machine Translation Evaluation without Error Annotations
Shaomu Tan, Ryosuke Mitani, Ritvik Choudhary +3
Over the years, automatic MT metrics have hillclimbed benchmarks and presented strong and sometimes human-level agreement with human ratings. Yet they remain black-box, offering li…
Investigating Test-Time Scaling with Reranking for Machine Translation
Shaomu Tan, Ryosuke Mitani, Ritvik Choudhary +1
Scaling model parameters has become the de facto strategy for improving NLP systems, but it comes with substantial computational costs. Test-Time Scaling (TTS) offers an alternativ…
Remedy: Learning Machine Translation Evaluation from Human Preferences with Reward Modeling
Shaomu Tan, Christof Monz
A key challenge in MT evaluation is the inherent noise and inconsistency of human ratings. Regression-based neural metrics struggle with this noise, while prompting LLMs shows prom…