57 citations · 165 across the 17 of their papers we have counts for
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cs.LG2023★ 12 cited
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness
Branislav Pecher, Ivan Srba, Maria Bielikova
Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-learning or few-shot learning, aims to effectively train a model using only a small a…
cs.CL2023★ 5 cited
KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection
Timo Hromadka, Timotej Smolen, Tomas Remis +2
This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of t…