7 citations · 8 across the 6 of their papers we have counts for
13 papers · 1 filter
Parameter-Efficient Retrievers for Polish and European Languages
Sławomir Dadas, Rafał Poświata, Małgorzata Grębowiec +1
Dense retrieval systems increasingly rely on multi-billion-parameter language models, whose memory and computational requirements make large-scale indexing, frequent corpus updates…
Polish ModernBERT: The Long and Short of Polish Language Understanding
Michał Perełkiewicz, Sławomir Dadas, Rafał Poświata +1
Encoder-only Transformers remain effective for discriminative and representation-learning tasks, yet Polish encoders still largely rely on BERT/RoBERTa-style architectures. We intr…
PUMA: A Polish Benchmark for Culturally Grounded Multimodal Understanding
Sławomir Dadas, Michał Perełkiewicz, Rafał Poświata +3
Large language models are increasingly moving beyond text processing, adding support for other modalities such as images and audio. While text understanding and generation have bee…
Long-Context Encoder Models for Polish Language Understanding
Sławomir Dadas, Rafał Poświata, Marek Kozłowski +4
While decoder-only Large Language Models (LLMs) have recently dominated the NLP landscape, encoder-only architectures remain a cost-effective and parameter-efficient standard for d…
PLLuM: A Family of Polish Large Language Models
Jan Kocoń, Maciej Piasecki, Arkadiusz Janz +96
Large Language Models (LLMs) play a central role in modern artificial intelligence, yet their development has been primarily focused on English, resulting in limited support for ot…
SMCLM: Semantically Meaningful Causal Language Modeling for Autoregressive Paraphrase Generation
Michał Perełkiewicz, Sławomir Dadas, Rafał Poświata
This article introduces semantically meaningful causal language modeling (SMCLM), a selfsupervised method of training autoregressive models to generate semantically equivalent text…