128 citations · 290 across the 17 of their papers we have counts for
4 papers · 1 filter
Demystifying Prompts in Language Models via Perplexity Estimation
Hila Gonen, Srini Iyer, Terra Blevins +2
Language models can be prompted to perform a wide variety of zero- and few-shot learning problems. However, performance varies significantly with the choice of prompt, and we do no…
Complementary Explanations for Effective In-Context Learning
Xi Ye, Srinivasan Iyer, Asli Celikyilmaz +3
Large language models (LLMs) have exhibited remarkable capabilities in learning from explanations in prompts, but there has been limited understanding of exactly how these explanat…
Improving In-Context Few-Shot Learning via Self-Supervised Training
Mingda Chen, Jingfei Du, Ramakanth Pasunuru +4
Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically adapted specifically for in-context few-shot le…
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
Badr AlKhamissi, Faisal Ladhak, Srini Iyer +7
Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next. It…