22 citations · 47 across the 16 of their papers we have counts for
18 papers
Reliability-Aware LLM Alignment from Inconsistent Human Feedback
Jingyi Huang, Ruohan Zong, Yujun Feng +3
Reinforcement Learning from Human Feedback (RLHF) is critical for aligning Large Language Models (LLMs) with human preferences. However, its efficacy is often compromised by the in…
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…
Designing Effective AI Explanations for Misinformation Detection: A Comparative Study of Content, Social, and Combined Explanations
Yeaeun Gong, Yifan Liu, Lanyu Shang +2
In this paper, we study the problem of AI explanation of misinformation, where the goal is to identify explanation designs that help improve users' misinformation detection abiliti…
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…
Hybrid Latent Reasoning via Reinforcement Learning
Zhenrui Yue, Bowen Jin, Huimin Zeng +6
Recent advances in large language models (LLMs) have introduced latent reasoning as a promising alternative to autoregressive reasoning. By performing internal computation with hid…
SIDE: Socially Informed Drought Estimation Toward Understanding Societal Impact Dynamics of Environmental Crisis
Lanyu Shang, Bozhang Chen, Shiwei Liu +6
Drought has become a critical global threat with significant societal impact. Existing drought monitoring solutions primarily focus on assessing drought severity using quantitative…
Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments
Zhenrui Yue, Huimin Zeng, Lanyu Shang +3
The rapid propagation of misinformation poses substantial risks to public interest. To combat misinformation, large language models (LLMs) are adapted to automatically verify claim…
Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation
Zhenrui Yue, Huimin Zeng, Yimeng Lu +3
The proliferation of online misinformation has posed significant threats to public interest. While numerous online users actively participate in the combat against misinformation,…
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning
Zhenrui Yue, Huimin Zeng, Yang Zhang +2
With emerging topics (e.g., COVID-19) on social media as a source for the spreading misinformation, overcoming the distributional shifts between the original training domain (i.e.,…
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks
Huimin Zeng, Zhenrui Yue, Yang Zhang +3
In many applications with real-world consequences, it is crucial to develop reliable uncertainty estimation for the predictions made by the AI decision systems. Targeting at the go…
Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup
Huimin Zeng, Zhenrui Yue, Ziyi Kou +3
In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the mo…
Domain Adaptation for Question Answering via Question Classification
Zhenrui Yue, Huimin Zeng, Ziyi Kou +2
Question answering (QA) has demonstrated impressive progress in answering questions from customized domains. Nevertheless, domain adaptation remains one of the most elusive challen…
Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19
Zhenrui Yue, Huimin Zeng, Ziyi Kou +2
Despite recent progress in improving the performance of misinformation detection systems, classifying misinformation in an unseen domain remains an elusive challenge. To address th…
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders
Zhenrui Yue, Huimin Zeng, Ziyi Kou +2
While sequential recommender systems achieve significant improvements on capturing user dynamics, we argue that sequential recommenders are vulnerable against substitution-based pr…
Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation
Zhenrui Yue, Huimin Zeng, Ziyi Kou +2
Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time. NILM, or energy…
AOMD: An Analogy-aware Approach to Offensive Meme Detection on Social Media
Lanyu Shang, Yang Zhang, Yuheng Zha +3
This paper focuses on an important problem of detecting offensive analogy meme on online social media where the visual content and the texts/captions of the meme together make an a…
FauxWard: A Graph Neural Network Approach to Fauxtography Detection Using Social Media Comments
Lanyu Shang, Yang Zhang, Daniel Zhang +1
Online social media has been a popular source for people to consume and share news content. More recently, the spread of misinformation online has caused widespread concerns. In th…
Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach
Lanyu Shang, Daniel Zhang, Michael Wang +2
Online video sharing platforms (e.g., YouTube, Vimeo) have become an increasingly popular paradigm for people to consume video contents. Clickbait video, whose content clearly devi…