2k citations · 6k across the 161 of their papers we have counts for
30 papers · 1 filter
Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning
Yao Zhu, Yuefeng Chen, Wei Wang +7
Learning generalized representations from limited training samples is crucial for applying deep neural networks in low-resource scenarios. Recently, methods based on Contrastive La…
Supervised Knowledge Makes Large Language Models Better In-context Learners
Linyi Yang, Shuibai Zhang, Zhuohao Yu +8
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The recent progress in large-scale generative models has further expanded th…
The Good, The Bad, and Why: Unveiling Emotions in Generative AI
Cheng Li, Jindong Wang, Yixuan Zhang +7
Emotion significantly impacts our daily behaviors and interactions. While recent generative AI models, such as large language models, have shown impressive performance in various t…
PromptBench: A Unified Library for Evaluation of Large Language Models
Kaijie Zhu, Qinlin Zhao, Hao Chen +2
The evaluation of large language models (LLMs) is crucial to assess their performance and mitigate potential security risks. In this paper, we introduce PromptBench, a unified libr…
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
Zhongyi Han, Guanglin Zhou, Rundong He +7
In machine learning, generalization against distribution shifts -- where deployment conditions diverge from the training scenarios -- is crucial, particularly in fields like climat…
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
Andy Zhou, Jindong Wang, Yu-Xiong Wang +1
We propose a conceptually simple and lightweight framework for improving the robustness of vision models through the combination of knowledge distillation and data augmentation. We…