8 papers
From Fuzzy Speech to Medical Insight: Benchmarking LLMs on Noisy Patient Narratives
Eden Mama, Liel Sheri, Yehudit Aperstein +1
The widespread adoption of large language models (LLMs) in healthcare raises critical questions about their ability to interpret patient-generated narratives, which are often infor…
When Curiosity Signals Danger: Predicting Health Crises Through Online Medication Inquiries
Dvora Goncharok, Arbel Shifman, Alexander Apartsin +1
Online medical forums are a rich and underutilized source of insight into patient concerns, especially regarding medication use. Some of the many questions users pose may signal co…
An Interpretable Benchmark for Clickbait Detection and Tactic Attribution
Lihi Nofar, Tomer Portal, Aviv Elbaz +2
The proliferation of clickbait headlines poses significant challenges to the credibility of information and user trust in digital media. While recent advances in machine learning h…
Multi-pathology Chest X-ray Classification with Rejection Mechanisms
Yehudit Aperstein, Amit Tzahar, Alon Gottlib +3
Overconfidence in deep learning models poses a significant risk in high-stakes medical imaging tasks, particularly in multi-label classification of chest X-rays, where multiple co-…
From Joy to Fear: A Benchmark of Emotion Estimation in Pop Song Lyrics
Shay Dahary, Avi Edana, Alexander Apartsin +1
The emotional content of song lyrics plays a pivotal role in shaping listener experiences and influencing musical preferences. This paper investigates the task of multi-label emoti…
Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant
Inbal Bolshinsky, Shani Kupiec, Almog Sasson +2
In the era of conversational AI, generating accurate and contextually appropriate service responses remains a critical challenge. A central question remains: Is explicit intent rec…