6 citations · 7 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
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…
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems
Marek Kadlčík, Michal Štefánik, Ondřej Sotolář +1
Despite outstanding performance in many tasks, language models are notoriously inclined to make factual errors in tasks requiring arithmetic computation. We address this deficiency…
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…