5 citations · 7 across the 7 of their papers we have counts for
7 papers
Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization
Weixu Zhang, Ye Yuan, Changjiang Han +7
Large Language Models (LLMs) exhibit strong implicit personalization ability, yet most existing approaches treat this behavior as a black box, relying on prompt engineering or fine…
Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired Decoding
Weixu Zhang, Fanghua Ye, Qiang Gao +7
Large language models (LLMs) often produce content that contradicts or overlooks information provided in the input context, a phenomenon known as faithfulness hallucination. In thi…
Understanding 6G through Language Models: A Case Study on LLM-aided Structured Entity Extraction in Telecom Domain
Ye Yuan, Haolun Wu, Hao Zhou +5
Knowledge understanding is a foundational part of envisioned 6G networks to advance network intelligence and AI-native network architectures. In this paradigm, information extracti…
What Makes Teamwork Work? A Multimodal Case Study on Emotions and Diagnostic Expertise in an Intelligent Tutoring System
Xiaoshan Huang, Haolun Wu, Xue Liu +1
Teamwork is pivotal in medical teamwork when professionals with diverse skills and emotional states collaborate to make critical decisions. This case study examines the interplay b…
Examining the Role of Peer Acknowledgements on Social Annotations: Unraveling the Psychological Underpinnings
Xiaoshan Huang, Haolun Wu, Xue Liu +1
This study explores the impact of peer acknowledgement on learner engagement and implicit psychological attributes in written annotations on an online social reading platform. Part…
Learning to Extract Structured Entities Using Language Models
Haolun Wu, Ye Yuan, Liana Mikaelyan +4
Recent advances in machine learning have significantly impacted the field of information extraction, with Language Models (LMs) playing a pivotal role in extracting structured info…
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
Haolun Wu, Ofer Meshi, Masrour Zoghi +4
Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, l…