1 citations · 1 across the 2 of their papers we have counts for
9 papers
Sigmoid Head for Quality Estimation under Language Ambiguity
Tu Anh Dinh, Jan Niehues
Language model (LM) probability is not a reliable quality estimator, as natural language is ambiguous. When multiple output options are valid, the model's probability distribution…
From Slides to Chatbots: Enhancing Large Language Models with University Course Materials
Tu Anh Dinh, Philipp Nicolas Schumacher, Jan Niehues
Large Language Models (LLMs) have advanced rapidly in recent years. One application of LLMs is to support student learning in educational settings. However, prior work has shown th…
KIT's Low-resource Speech Translation Systems for IWSLT2025: System Enhancement with Synthetic Data and Model Regularization
Zhaolin Li, Yining Liu, Danni Liu +6
This paper presents KIT's submissions to the IWSLT 2025 low-resource track. We develop both cascaded systems, consisting of Automatic Speech Recognition (ASR) and Machine Translati…
Are Generative Models Underconfident? Better Quality Estimation with Boosted Model Probability
Tu Anh Dinh, Jan Niehues
Quality Estimation (QE) is estimating quality of the model output during inference when the ground truth is not available. Deriving output quality from the models' output probabili…
COMET-poly: Machine Translation Metric Grounded in Other Candidates
Maike Züfle, Vilém Zouhar, Tu Anh Dinh +3
Automated metrics for machine translation attempt to replicate human judgment. Unlike humans, who often assess a translation in the context of multiple alternatives, these metrics…
Knockout LLM Assessment: Using Large Language Models for Evaluations through Iterative Pairwise Comparisons
Isik Baran Sandan, Tu Anh Dinh, Jan Niehues
Large Language Models (LLMs) have shown to be effective evaluators across various domains such as machine translations or the scientific domain. Current LLM-as-a-Judge approaches r…