2.3k citations · 2.5k across the 38 of their papers we have counts for
10 papers · 1 filter
NegVQA: Can Vision Language Models Understand Negation?
Yuhui Zhang, Yuchang Su, Yiming Liu +1
Negation is a fundamental linguistic phenomenon that can entirely reverse the meaning of a sentence. As vision language models (VLMs) continue to advance and are deployed in high-s…
Can Large Language Models Match the Conclusions of Systematic Reviews?
Christopher Polzak, Alejandro Lozano, Min Woo Sun +4
Systematic reviews (SR), in which experts summarize and analyze evidence across individual studies to provide insights on a specialized topic, are a cornerstone for evidence-based…
A Large-Scale Vision-Language Dataset Derived from Open Scientific Literature to Advance Biomedical Generalist AI
Alejandro Lozano, Min Woo Sun, James Burgess +16
Despite the excitement behind biomedical artificial intelligence (AI), access to high-quality, diverse, and large-scale data - the foundation for modern AI systems - is still a bot…
MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks
Allen Nie, Yuhui Zhang, Atharva Amdekar +3
Human commonsense understanding of the physical and social world is organized around intuitive theories. These theories support making causal and moral judgments. When something ba…
Inverse Scaling: When Bigger Isn't Better
Ian R. McKenzie, Alexander Lyzhov, Michael Pieler +24
Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, w…
Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models
Yuhui Zhang, Michihiro Yasunaga, Zhengping Zhou +4
Language models have been shown to exhibit positive scaling, where performance improves as models are scaled up in terms of size, compute, or data. In this work, we introduce NeQA,…
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning
Weixin Liang, Yuhui Zhang, Yongchan Kwon +2
We present modality gap, an intriguing geometric phenomenon of the representation space of multi-modal models. Specifically, we show that different data modalities (e.g. images and…
Biomedical and Clinical English Model Packages in the Stanza Python NLP Library
Yuhao Zhang, Yuhui Zhang, Peng Qi +2
We introduce biomedical and clinical English model packages for the Stanza Python NLP library. These packages offer accurate syntactic analysis and named entity recognition capabil…
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
Peng Qi, Yuhao Zhang, Yuhui Zhang +2
We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages. Compared to existing widely used toolkits, Stanza features a language-…
Large-scale Generative Modeling to Improve Automated Veterinary Disease Coding
Yuhui Zhang, Allen Nie, James Zou
Supervised learning is limited both by the quantity and quality of the labeled data. In the field of medical record tagging, writing styles between hospitals vary drastically. The…