5 citations · 5 across the 7 of their papers we have counts for
8 papers
Disparities In Negation Understanding Across Languages In Vision-Language Models
Charikleia Moraitaki, Sarah Pan, Skyler Pulling +3
Vision-language models (VLMs) exhibit affirmation bias: a systematic tendency to select positive captions ("X is present") even when the correct description contains negation ("no…
Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
Kimia Hamidieh, Veronika Thost, Walter Gerych +2
Large language models (LLMs) often produce confident yet incorrect responses, and uncertainty quantification is one potential solution to more robust usage. Recent works routinely…
Reaching Beyond the Mode: RL for Distributional Reasoning in Language Models
Isha Puri, Mehul Damani, Idan Shenfeld +3
Given a question, a language model (LM) implicitly encodes a distribution over possible answers. In practice, post-training procedures for LMs often collapse this distribution onto…
MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations
Genglin Liu, Vivian Le, Salman Rahman +3
We present a novel, open-source social network simulation framework, MOSAIC, where generative language agents predict user behaviors such as liking, sharing, and flagging content.…
Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions
Yik Siu Chan, Narutatsu Ri, Yuxin Xiao +1
Despite extensive safety alignment efforts, large language models (LLMs) remain vulnerable to jailbreak attacks that elicit harmful behavior. While existing studies predominantly f…
MisinfoEval: Generative AI in the Era of "Alternative Facts"
Saadia Gabriel, Liang Lyu, James Siderius +3
The spread of misinformation on social media platforms threatens democratic processes, contributes to massive economic losses, and endangers public health. Many efforts to address…
SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe
Yuxin Xiao, Shujian Zhang, Wenxuan Zhou +2
To acquire instruction-following capabilities, large language models (LLMs) undergo instruction tuning, where they are trained on instruction-response pairs using next-token predic…
Can AI Relate: Testing Large Language Model Response for Mental Health Support
Saadia Gabriel, Isha Puri, Xuhai Xu +2
Large language models (LLMs) are already being piloted for clinical use in hospital systems like NYU Langone, Dana-Farber and the NHS. A proposed deployment use case is psychothera…