7 citations · 18 across the 24 of their papers we have counts for
10 papers · 1 filter
Steering Vision-Language Models for Visual-Contextual Privacy in Location Disclosure
Wenyue Chen, Yuhan Cheng, Ruixin Yang +5
Vision-language models (VLMs) can infer sensitive information such as geolocation from subtle visual cues, creating privacy risks when responses disclose more detail than the visua…
Synthetic Worlds for Temporal Evaluation and Knowledge Updating in LLMs
Jonathan Zheng, Zirui Shao, Alan Ritter +1
Large language models (LLMs) rely on static pretraining corpora, causing their knowledge to become outdated over time. Existing approaches for evaluating knowledge edits either suf…
CoCoA: Context-Conditional Cultural Alignment for Large Language Models
Kyungdon Lee, Wei Xu, Alan Ritter +2
Large Language Models (LLMs) often favor Western-associated entities across cultural contexts. Conventional debiasing methods aim for uniform neutrality, but cultural bias mitigati…
Soft Token Alignment for Cross-Lingual Reasoning
Jiayi He, Jungsoo Park, Wei Xu +1
Multilingual large language models often produce inconsistent reasoning and answers for semantically equivalent prompts in different languages. Prior work suggests that intermediat…
Investigating and Alleviating Harm Amplification in LLM Interactions
Ruohao Guo, Wei Xu, Alan Ritter
Large language models (LLMs) can serve as helpful assistants, yet they can equally function as harm amplifiers that enable malicious users to achieve harmful outcomes beyond their…
Learning to Route Languages for Multilingual Policy Optimization
Geyang Guo, Hiromi Wakaki, Yuki Mitsufuji +2
Large language models~(LLMs) are trained on heterogeneous multilingual corpora, yet existing policy optimization methods often implicitly restrict each training question to a singl…
Reinforcement Learning over Predictive Distributions for LLM Regression
Jungsoo Park, Hyungjoo Chae, Ethan Mendes +4
Large language models (LLMs) have emerged as flexible regressors capable of predicting real-valued quantities from heterogeneous inputs. Yet most LLM regression objectives optimize…
Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?
Ruixin Yang, Ethan Mendes, Arthur Wang +4
Vision-language models (VLMs) have demonstrated strong performance in image geolocation, a capability further sharpened by frontier multimodal large reasoning models (MLRMs). This…
Supporting Informed Self-Disclosure: Design Recommendations for Presenting AI-Estimates of Privacy Risks to Users
Isadora Krsek, Meryl Ye, Wei Xu +3
People candidly discuss sensitive topics online under the perceived safety of anonymity; yet, for many, this perceived safety is tenuous, as miscalibrated risk perceptions can lead…
GeoRC: A Benchmark for Geolocation Reasoning Chains
Mohit Talreja, Joshua Diao, Jim Thannikary James +6
Vision Language Models (VLMs) are good at recognizing the global location of a photograph -- their geolocation prediction accuracy rivals the best human experts. But many VLMs are…