6 citations · 8 across the 5 of their papers we have counts for
5 papers
Benchmarking LLMs for Political Science: A United Nations Perspective
Yueqing Liang, Liangwei Yang, Chen Wang +6
Large Language Models (LLMs) have achieved significant advances in natural language processing, yet their potential for high-stake political decision-making remains largely unexplo…
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
Yuwei Cao, Liangwei Yang, Zhiwei Liu +5
Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address thi…
Taxonomy-Guided Zero-Shot Recommendations with LLMs
Yueqing Liang, Liangwei Yang, Chen Wang +3
With the emergence of large language models (LLMs) and their ability to perform a variety of tasks, their application in recommender systems (RecSys) has shown promise. However, we…
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction
Chen Wang, Fangxin Wang, Ruocheng Guo +2
In Sequential Recommendation Systems (SRecsys), traditional training approaches that rely on Cross-Entropy (CE) loss often prioritize accuracy but fail to align well with user sati…
Collaborative Semantic Alignment in Recommendation Systems
Chen Wang, Liangwei Yang, Zhiwei Liu +4
Traditional recommender systems primarily leverage identity-based (ID) representations for users and items, while the advent of pre-trained language models (PLMs) has introduced ri…