164 citations · 501 across the 21 of their papers we have counts for
6 papers · 1 filter
Structured Prompting: Scaling In-Context Learning to 1,000 Examples
Yaru Hao, Yutao Sun, Li Dong +3
Large language models have exhibited intriguing in-context learning capability, achieving promising zero- and few-shot performance without updating the parameters. However, convent…
Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers
Damai Dai, Yutao Sun, Li Dong +4
Large pretrained language models have shown surprising in-context learning (ICL) ability. With a few demonstration input-label pairs, they can predict the label for an unseen input…
Optimizing Prompts for Text-to-Image Generation
Yaru Hao, Zewen Chi, Li Dong +1
Well-designed prompts can guide text-to-image models to generate amazing images. However, the performant prompts are often model-specific and misaligned with user input. Instead of…
Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes
Yiqiao Jin, Xiting Wang, Yaru Hao +2
In this paper, we move towards combining large parametric models with non-parametric prototypical networks. We propose prototypical fine-tuning, a novel prototypical framework for…
Language Models are General-Purpose Interfaces
Yaru Hao, Haoyu Song, Li Dong +5
Foundation models have received much attention due to their effectiveness across a broad range of downstream applications. Though there is a big convergence in terms of architectur…
Prototypical Calibration for Few-shot Learning of Language Models
Zhixiong Han, Yaru Hao, Li Dong +2
In-context learning of GPT-like models has been recognized as fragile across different hand-crafted templates, and demonstration permutations. In this work, we propose prototypical…