11 citations · 39 across the 8 of their papers we have counts for
8 papers
AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
Hao Zhou, Wenge Rong, Jianfei Zhang +3
Knowledge Tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant…
Explainable Few-shot Knowledge Tracing
Haoxuan Li, Jifan Yu, Yuanxin Ouyang +4
Knowledge tracing (KT), aiming to mine students' mastery of knowledge by their exercise records and predict their performance on future test questions, is a critical task in educat…
Wasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty
Haoxuan Li, Yuanxin Ouyang, Zhuang Liu +2
Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF…
A Review of Data Mining in Personalized Education: Current Trends and Future Prospects
Zhang Xiong, Haoxuan Li, Zhuang Liu +4
Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness…
Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering
Keheng Wang, Feiyu Duan, Sirui Wang +5
Equipped with Chain-of-Thought (CoT), Large language models (LLMs) have shown impressive reasoning ability in various downstream tasks. Even so, suffering from hallucinations and t…
Transformer-Patcher: One Mistake worth One Neuron
Zeyu Huang, Yikang Shen, Xiaofeng Zhang +3
Large Transformer-based Pretrained Language Models (PLMs) dominate almost all Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes from time to time. For…
Mixture of Attention Heads: Selecting Attention Heads Per Token
Xiaofeng Zhang, Yikang Shen, Zeyu Huang +3
Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. However, the study of MoE components mostly…
Causal Intervention for Fairness in Multi-behavior Recommendation
Xi Wang, Wenjie Wang, Fuli Feng +3
Recommender systems usually learn user interests from various user behaviors, including clicks and post-click behaviors (e.g., like and favorite). However, these behaviors inevitab…