30 citations · 69 across the 15 of their papers we have counts for
15 papers
Where Do Apparent LLM Clinical Triage Failures Arise? Localizing the Multiple-Choice Format Effect
David Fraile Navarro, Berardino Como, Jialei Sheng +2
LLM evaluations using clinician-authored triage vignettes have reported substantial under-triage under constrained multiple-choice testing. Yet model performance on the same clinic…
Recommendation-as-Experience: A framework for context-sensitive adaptation in conversational recommender systems
Raj Mahmud, Shlomo Berkovsky, Mukesh Prasad +1
While Conversational Recommender Systems (CRS) have matured technically, they frequently lack principled methods for encoding latent experiential aims as adaptive state variables.…
Uncertainty-Aware Dynamic Knowledge Graphs for Reliable Question Answering
Yu Takahashi, Shun Takeuchi, Kexuan Xin +6
Question answering (QA) systems are increasingly deployed across domains. However, their reliability is undermined when retrieved evidence is incomplete, noisy, or uncertain. Exist…
Evaluating User Experience in Conversational Recommender Systems: A Systematic Review Across Classical and LLM-Powered Approaches
Raj Mahmud, Yufeng Wu, Abdullah Bin Sawad +3
Conversational Recommender Systems (CRSs) are receiving growing research attention across domains, yet their user experience (UX) evaluation remains limited. Existing reviews large…
Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits
Raj Mahmud, Shlomo Berkovsky, Mukesh Prasad +1
Conversational Recommender Systems (CRSs) deliver personalised recommendations through multi-turn natural language dialogue and increasingly support both task-oriented and explorat…
Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification
Soham Rohit Chitnis, Sidong Liu, Tirtharaj Dash +5
Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and…