163 citations · 308 across the 6 of their papers we have counts for
6 papers
When Surfaces Lie: Exploiting Wrinkle-Induced Attention Shift to Attack Vision-Language Models
Chengyin Hu, Xuemeng Sun, Jiaju Han +5
Visual-Language Models (VLMs) have demonstrated exceptional cross-modal understanding across various tasks, including zero-shot classification, image captioning, and visual questio…
MathGen: Revealing the Illusion of Mathematical Competence through Text-to-Image Generation
Ruiyao Liu, Hui Shen, Ping Zhang +16
Modern generative models have demonstrated the ability to solve challenging mathematical problems. In many real-world settings, however, mathematical solutions must be expressed vi…
Phi-4 Technical Report
Marah Abdin, Jyoti Aneja, Harkirat Behl +24
We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models, where pre-training…
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Marah Abdin, Jyoti Aneja, Hany Awadalla +126
We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal test…
Textbooks Are All You Need
Suriya Gunasekar, Yi Zhang, Jyoti Aneja +16
We introduce phi-1, a new large language model for code, with significantly smaller size than competing models: phi-1 is a Transformer-based model with 1.3B parameters, trained for…
Controllable Text-to-Image Generation with GPT-4
Tianjun Zhang, Yi Zhang, Vibhav Vineet +2
Current text-to-image generation models often struggle to follow textual instructions, especially the ones requiring spatial reasoning. On the other hand, Large Language Models (LL…