6 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.CL2023★ 6 cited
Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation
Marius Mosbach, Tiago Pimentel, Shauli Ravfogel +2
Few-shot fine-tuning and in-context learning are two alternative strategies for task adaptation of pre-trained language models. Recently, in-context learning has gained popularity…
cs.CL2022★ 1 cited
Fusing Sentence Embeddings Into LSTM-based Autoregressive Language Models
Vilém Zouhar, Marius Mosbach, Dietrich Klakow
Although masked language models are highly performant and widely adopted by NLP practitioners, they can not be easily used for autoregressive language modelling (next word predicti…