9 citations · 13 across the 3 of their papers we have counts for
Showing cs.CLShow all
2 papers · 1 filter
cs.CL2022★ 4 cited
Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task
Guilherme Moraes Rosa, Luiz Bonifacio, Vitor Jeronymo +3
Recent work has shown that language models scaled to billions of parameters, such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios. In this work, we experiment…
cs.CL2022★ 9 cited
To Tune or Not To Tune? Zero-shot Models for Legal Case Entailment
Guilherme Moraes Rosa, Ruan Chaves Rodrigues, Roberto de Alencar Lotufo +1
There has been mounting evidence that pretrained language models fine-tuned on large and diverse supervised datasets can transfer well to a variety of out-of-domain tasks. In this…