3 citations · 3 across the 7 of their papers we have counts for
7 papers
Test-Time Hinting for Black-Box Vision-Language Models
Kaihua Hou, Abhijith Varma Mudunuri, Jiaxing Qiu +3
Test-time scaling (TTS) methods have proven highly effective for LLMs, yet their application to vision-language models (VLMs) remains relatively underexplored. Existing VLM TTS met…
Aligning Language Model Benchmarks with Pairwise Preferences
Marco Gutierrez, Xinyi Leng, Hannah Cyberey +3
Language model benchmarks are pervasive and computationally-efficient proxies for real-world downstream performance. However, many recent works find that benchmarks often fail to p…
ReasonEdit: Editing Vision-Language Models using Human Reasoning
Jiaxing Qiu, Kaihua Hou, Roxana Daneshjou +2
Model editing aims to correct errors in large, pretrained models without altering unrelated behaviors. While some recent works have edited vision-language models (VLMs), no existin…
A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models
Sonali Sharma, Ahmed M. Alaa, Roxana Daneshjou
Generative AI models, including large language models (LLMs) and vision-language models (VLMs), are increasingly used to interpret medical images and answer clinical questions. The…
Model Editing with Graph-Based External Memory
Yash Kumar Atri, Ahmed Alaa, Thomas Hartvigsen
Large language models (LLMs) have revolutionized natural language processing, yet their practical utility is often limited by persistent issues of hallucinations and outdated param…
Norm Growth and Stability Challenges in Localized Sequential Knowledge Editing
Akshat Gupta, Christine Fang, Atahan Ozdemir +4
This study investigates the impact of localized updates to large language models (LLMs), specifically in the context of knowledge editing - a task aimed at incorporating or modifyi…
Lifelong Knowledge Editing requires Better Regularization
Akshat Gupta, Phudish Prateepamornkul, Maochuan Lu +3
Knowledge editing is a promising way to improve factuality in large language models, but recent studies have shown significant model degradation during sequential editing. In this…