5 citations · 5 across the 6 of their papers we have counts for
6 papers
Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning
Jiachen Zhu, Congmin Zheng, Jianghao Lin +5
While large language models (LLMs) have significantly advanced mathematical reasoning, Process Reward Models (PRMs) have been developed to evaluate the logical validity of reasonin…
Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
Yunjia Xi, Muyan Weng, Wen Chen +9
Recommender systems (RSs) often suffer from the feedback loop phenomenon, e.g., RSs are trained on data biased by their recommendations. This leads to the filter bubble effect that…
Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation
Kounianhua Du, Hanjing Wang, Jianxing Liu +7
Large language models (LLMs) have demonstrated remarkable capabilities in various domains, particularly in system 1 tasks, yet the intricacies of their problem-solving mechanisms i…
Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
Rong Shan, Jiachen Zhu, Jianghao Lin +5
In this paper, we address the lifelong sequential behavior incomprehension problem in large language models (LLMs) for recommendation, where LLMs struggle to extract useful informa…
World Model-based Perception for Visual Legged Locomotion
Hang Lai, Jiahang Cao, Jiafeng Xu +5
Legged locomotion over various terrains is challenging and requires precise perception of the robot and its surroundings from both proprioception and vision. However, learning dire…
A Survey on Diffusion Models for Recommender Systems
Jianghao Lin, Jiaqi Liu, Jiachen Zhu +5
While traditional recommendation techniques have made significant strides in the past decades, they still suffer from limited generalization performance caused by factors like inad…