9 papers
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…
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…
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…