29 citations · 40 across the 13 of their papers we have counts for
3 papers · 1 filter
When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations
Aleksandar Petrov, Philip H. S. Torr, Adel Bibi
Context-based fine-tuning methods, including prompting, in-context learning, soft prompting (also known as prompt tuning), and prefix-tuning, have gained popularity due to their ab…
Language Model Tokenizers Introduce Unfairness Between Languages
Aleksandar Petrov, Emanuele La Malfa, Philip H. S. Torr +1
Recent language models have shown impressive multilingual performance, even when not explicitly trained for it. Despite this, there are concerns about the quality of their outputs…
Certifying Ensembles: A General Certification Theory with S-Lipschitzness
Aleksandar Petrov, Francisco Eiras, Amartya Sanyal +2
Improving and guaranteeing the robustness of deep learning models has been a topic of intense research. Ensembling, which combines several classifiers to provide a better model, ha…