75 citations · 80 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 3 cited
The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry
Michael Zhang, Kush Bhatia, Hermann Kumbong +1
Linear attentions have shown potential for improving Transformer efficiency, reducing attention's quadratic complexity to linear in sequence length. This holds exciting promise for…
cs.CL2023★ 2 cited
SuperHF: Supervised Iterative Learning from Human Feedback
Gabriel Mukobi, Peter Chatain, Su Fong +4
While large language models demonstrate remarkable capabilities, they often present challenges in terms of safety, alignment with human values, and stability during training. Here,…
cs.CL2022★ 75 cited
Ask Me Anything: A simple strategy for prompting language models
Simran Arora, Avanika Narayan, Mayee F. Chen +6
Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training.…