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cs.CL2024
Self-training Language Models for Arithmetic Reasoning
Marek Kadlčík, Michal Štefánik
Recent language models achieve impressive results in tasks involving complex multistep reasoning, but scaling these capabilities further traditionally requires expensive collection…
cs.CL2024
Concept-aware Data Construction Improves In-context Learning of Language Models
Michal Štefánik, Marek Kadlčík, Petr Sojka
Many recent language models (LMs) are capable of in-context learning (ICL), manifested in the LMs' ability to perform a new task solely from natural-language instruction. Previous…