4 citations · 4 across the 7 of their papers we have counts for
11 papers
PropUQ-MAS: Propagation-Aware Uncertainty Quantification for LLM Multi-Agent Systems
Yaokun Liu, Yifan Liu, Daniel Yue Zhang +3
LLM-based multi-agent systems (MAS) solve complex tasks through communication among role-specialized agents. However, inter-agent dependencies introduce reliability risks beyond is…
Beyond Truth Discovery: A Two-Stage Framework to Assess the Severity of False Claim during Disasters
Ruichen Yao, Tejna Dasari, Gulshat Baispay +5
False information spreads rapidly on social media during disasters and can undermine emergency response efforts, public trust, and crisis communication. Existing research primarily…
Topology-Aware Tokenization for Generative Recommendation
Yaokun Liu, Yifan Liu, Zhenrui Yue +4
Generative recommendation reformulates sequential recommendation as an autoregressive generation task, yet a critical issue in this paradigm remains overlooked: topology distortion…
DisImpact: Quantifying the Physi-Social Impact of Natural Disasters Through Social Media
Ruichen Yao, Tejna Dasari, Xuanyu Meng +6
Natural disasters not only cause large-scale physical destruction, but also cascading social consequences that are difficult to quantify with traditional surveys and reports. Socia…
Mind the Ambiguity: Aleatoric Uncertainty Quantification in LLMs for Safe Medical Question Answering
Yaokun Liu, Yifan Liu, Phoebe Mbuvi +4
The deployment of Large Language Models in Medical Question Answering is severely hampered by ambiguous user queries, a significant safety risk that demonstrably reduces answer acc…
MASH: A Multiplatform and Multimodal Annotated Dataset for Societal Impact of Hurricane
Ruichen Yao, Aslanbek Murzakhmetov, Raaghav Pillai +9
Natural disasters cause multidimensional threats to human societies, with hurricanes exemplifying one of the most disruptive events that not only caused severe physical damage but…
Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation
Yifan Liu, Yaokun Liu, Zelin Li +5
Recent advances in generative recommenders adopt a two-stage paradigm: items are first tokenized into semantic IDs using a pretrained tokenizer, and then large language models (LLM…
Anti-Tamper Protection for Unauthorized Individual Image Generation
Zelin Li, Ruohan Zong, Yifan Liu +4
With the advancement of personalized image generation technologies, concerns about forgery attacks that infringe on portrait rights and privacy are growing. To address these concer…
Component-Based Fairness in Face Attribute Classification with Bayesian Network-informed Meta Learning
Yifan Liu, Ruichen Yao, Yaokun Liu +4
The widespread integration of face recognition technologies into various applications (e.g., access control and personalized advertising) necessitates a critical emphasis on fairne…
Modality Interactive Mixture-of-Experts for Fake News Detection
Yifan Liu, Yaokun Liu, Zelin Li +3
The proliferation of fake news on social media platforms disproportionately impacts vulnerable populations, eroding trust, exacerbating inequality, and amplifying harmful narrative…
Improving Fairness in Image Classification via Sketching
Ruichen Yao, Ziteng Cui, Xiaoxiao Li +1
Fairness is a fundamental requirement for trustworthy and human-centered Artificial Intelligence (AI) system. However, deep neural networks (DNNs) tend to make unfair predictions w…