2 citations · 2 across the 4 of their papers we have counts for
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…
Status of the Trinity PeV Neutrino Observatory
S. Stepanoff, A. N. Otte, M. Bagheri +29
The Trinity Neutrino Observatory aims to detect tau neutrinos in the energy range of 1 PeV to 10 EeV. We are developing the observatory in three stages. The first stage, known as t…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
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…