7 papers
Positional Failures in Long-Context LLMs: A Blind Spot in Reasoning Benchmarks
Chuyifei Zhang, Hongyu Cui, Xiaowen Huang +1
Position-controlled evaluation is standard for retrieval tasks such as Needle-in-a-Haystack and RULER, but mainstream reasoning benchmarks do not control positional placement of ta…
Membership Inference Attack against Large Language Model-based Recommendation Systems: A New Distillation-based Paradigm
Li Cuihong, Huang Xiaowen, Yin Chuanhuan +1
Membership Inference Attack (MIA) aims to determine whether a specific data sample was included in the training dataset of a target model. Traditional MIA approaches rely on shadow…
ITDR: An Instruction Tuning Dataset for Enhancing Large Language Models in Recommendations
Zekun Liu, Xiaowen Huang, Jitao Sang
Large language models (LLMs) have demonstrated outstanding performance in natural language processing tasks. However, in the field of recommender systems, due to the inherent struc…
Understanding the Information Cocoon: A Multidimensional Assessment and Analysis of News Recommendation Systems
Xin Wang, Xiaowen Huang, Jitao Sang
Personalized news recommendation systems inadvertently create information cocoons--homogeneous information bubbles that reinforce user biases and amplify societal polarization. To…
When Algorithms Mirror Minds: A Confirmation-Aware Social Dynamic Model of Echo Chamber and Homogenization Traps
Ming Tang, Xiaowen Huang, Jitao Sang
Recommender systems increasingly suffer from echo chambers and user homogenization, systemic distortions arising from the dynamic interplay between algorithmic recommendations and…
Mitigating Filter Bubble from the Perspective of Community Detection: A Universal Framework
Ming Tang, Xiaowen Huang, Jitao Sang
In recent years, recommender systems have primarily focused on improving accuracy at the expense of diversity, which exacerbates the well-known filter bubble effect. This paper pro…