1 citations · 1 across the 4 of their papers we have counts for
9 papers · 1 filter
Ladders in Chaos: When, How, (and Perhaps Why) Does Test-Time Scaling Improve LLM Machine Translation
Di Wu, Sergey Troshin, Christof Monz +2
Two forms of test-time scaling for Large Language Models (LLMs) have emerged as effective and widely adopted paradigms: sequential, in which later answer attempts depend on earlier…
Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps Translation
Di Wu, Seth Aycock, Christof Monz
Large Language Models (LLMs) demonstrate strong reasoning capabilities for many tasks, often by explicitly decomposing the task via Chain-of-Thought (CoT) reasoning. Recent work on…
What Does Neuro Mean to Cardio? Investigating the Role of Clinical Specialty Data in Medical LLMs
Xinlan Yan, Di Wu, Yibin Lei +2
In this paper, we introduce S-MedQA, an English medical question-answering (QA) dataset designed for benchmarking large language models (LLMs) in fine-grained clinical specialties.…
Calibrating Translation Decoding with Quality Estimation on LLMs
Di Wu, Yibin Lei, Christof Monz
Neural machine translation (NMT) systems typically employ maximum a posteriori (MAP) decoding to select the highest-scoring translation from the distribution mass. However, recent…
Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?
Seth Aycock, David Stap, Di Wu +2
Extremely low-resource (XLR) languages lack substantial corpora for training NLP models, motivating the use of all available resources such as dictionaries and grammar books. Machi…
How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translation
Yan Meng, Di Wu, Christof Monz
The massive amounts of web-mined parallel data contain large amounts of noise. Semantic misalignment, as the primary source of the noise, poses a challenge for training machine tra…