3 citations · 4 across the 6 of their papers we have counts for
6 papers
Prompts Without Evidence: How Neuroimaging Mentions Shift Clinical Vision-Language Model Predictions
Doan Nam Long Vu, Simone Balloccu
Trustworthy clinical AI must use real evidence and avoid relying on surface-level artifacts. We evaluate 12 open-weight vision-language models (VLMs) on two clinical neuroimaging c…
Roleplaying with Structure: Synthetic Therapist-Client Conversation Generation from Questionnaires
Doan Nam Long Vu, Rui Tan, Lena Moench +10
Large Language Models (LLMs) are promising tools for synthetic data generation in mental health. However, privacy policies and restrictions forced previous work to rely mainly on g…
A Course Shared Task on Evaluating LLM Output for Clinical Questions
Yufang Hou, Thy Thy Tran, Doan Nam Long Vu +4
This paper presents a shared task that we organized at the Foundations of Language Technology (FoLT) course in 2023/2024 at the Technical University of Darmstadt, which focuses on…
Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation
Doan Nam Long Vu, Timour Igamberdiev, Ivan Habernal
Applying differential privacy (DP) by means of the DP-SGD algorithm to protect individual data points during training is becoming increasingly popular in NLP. However, the choice o…
DP-NMT: Scalable Differentially-Private Machine Translation
Timour Igamberdiev, Doan Nam Long Vu, Felix Künnecke +3
Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite sig…
Layer or Representation Space: What makes BERT-based Evaluation Metrics Robust?
Doan Nam Long Vu, Nafise Sadat Moosavi, Steffen Eger
The evaluation of recent embedding-based evaluation metrics for text generation is primarily based on measuring their correlation with human evaluations on standard benchmarks. How…