6 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.CL2023
Concept-aware Training Improves In-context Learning Ability of Language Models
Michal Štefánik, Marek Kadlčík
Many recent language models (LMs) of Transformers family exhibit so-called in-context learning (ICL) ability, manifested in the LMs' ability to modulate their function by a task de…
cs.SD2023★ 6 cited
A Whisper transformer for audio captioning trained with synthetic captions and transfer learning
Marek Kadlčík, Adam Hájek, Jürgen Kieslich +1
The field of audio captioning has seen significant advancements in recent years, driven by the availability of large-scale audio datasets and advancements in deep learning techniqu…
cs.CL2023
Resources and Few-shot Learners for In-context Learning in Slavic Languages
Michal Štefánik, Marek Kadlčík, Piotr Gramacki +1
Despite the rapid recent progress in creating accurate and compact in-context learners, most recent work focuses on in-context learning (ICL) for tasks in English. However, the abi…