23 citations · 47 across the 12 of their papers we have counts for
14 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…
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…
The PLLuM Instruction Corpus
Piotr Pęzik, Filip Żarnecki, Konrad Kaczyński +50
This paper describes the instruction dataset used to fine-tune a set of transformer-based large language models (LLMs) developed in the PLLuM (Polish Large Language Model) project.…
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…
Unveiling Dual Quality in Product Reviews: An NLP-Based Approach
Rafał Poświata, Marcin Michał Mirończuk, Sławomir Dadas +2
Consumers often face inconsistent product quality, particularly when identical products vary between markets, a situation known as the dual quality problem. To identify and address…
Evaluating Polish linguistic and cultural competency in large language models
Sławomir Dadas, Małgorzata Grębowiec, Michał Perełkiewicz +1
Large language models (LLMs) are becoming increasingly proficient in processing and generating multilingual texts, which allows them to address real-world problems more effectively…
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
A Review of the Challenges with Massive Web-mined Corpora Used in Large Language Models Pre-Training
Michał Perełkiewicz, Rafał Poświata
This article presents a comprehensive review of the challenges associated with using massive web-mined corpora for the pre-training of large language models (LLMs). This review ide…
PL-MTEB: Polish Massive Text Embedding Benchmark
Rafał Poświata, Sławomir Dadas, Michał Perełkiewicz
In this paper, we introduce the Polish Massive Text Embedding Benchmark (PL-MTEB), a comprehensive benchmark for text embeddings in the Polish language. PL-MTEB comprises 30 divers…
PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods
Sławomir Dadas, Michał Perełkiewicz, Rafał Poświata
We present Polish Information Retrieval Benchmark (PIRB), a comprehensive evaluation framework encompassing 41 text information retrieval tasks for Polish. The benchmark incorporat…
Pre-training Polish Transformer-based Language Models at Scale
Sławomir Dadas, Michał Perełkiewicz, Rafał Poświata
Transformer-based language models are now widely used in Natural Language Processing (NLP). This statement is especially true for English language, in which many pre-trained models…
Evaluation of Sentence Representations in Polish
Sławomir Dadas, Michał Perełkiewicz, Rafał Poświata
Methods for learning sentence representations have been actively developed in recent years. However, the lack of pre-trained models and datasets annotated at the sentence level has…