5 papers
Visual Attention Faithfulness in Vision-Language Models is Heterogeneous
Xurui Song, Weishi Wang, Zhongqi Yue +5
Whether attention weights faithfully reflect model reasoning has been actively debated in NLP, yet this question remains largely unexplored for the visual modality in Vision-Langua…
MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues
Kuluhan Binici, Abhinav Ramesh Kashyap, Viktor Schlegel +6
Automatic Speech Recognition (ASR) systems are pivotal in transcribing speech into text, yet the errors they introduce can significantly degrade the performance of downstream tasks…
Condensed Data Expansion Using Model Inversion for Knowledge Distillation
Kuluhan Binici, Shivam Aggarwal, Cihan Acar +4
Condensed datasets offer a compact representation of larger datasets, but training models directly on them or using them to enhance model performance through knowledge distillation…
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction
Aishik Nagar, Viktor Schlegel, Thanh-Tung Nguyen +4
Large Language Models (LLMs) are increasingly adopted for applications in healthcare, reaching the performance of domain experts on tasks such as question answering and document su…
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student Architectures
Kuluhan Binici, Weiming Wu, Tulika Mitra
Knowledge distillation (KD) is a model compression method that entails training a compact student model to emulate the performance of a more complex teacher model. However, the arc…