7 citations · 15 across the 15 of their papers we have counts for
18 papers
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…
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…
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…
Assessing generalization capability of text ranking models in Polish
Sławomir Dadas, Małgorzata Grębowiec
Retrieval-augmented generation (RAG) is becoming an increasingly popular technique for integrating internal knowledge bases with large language models. In a typical RAG pipeline, t…
OPI at SemEval 2023 Task 9: A Simple But Effective Approach to Multilingual Tweet Intimacy Analysis
Sławomir Dadas
This paper describes our submission to the SemEval 2023 multilingual tweet intimacy analysis shared task. The goal of the task was to assess the level of intimacy of Twitter posts…
OPI at SemEval 2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation
Sławomir Dadas
The goal of visual word sense disambiguation is to find the image that best matches the provided description of the word's meaning. It is a challenging problem, requiring approache…
Training Effective Neural Sentence Encoders from Automatically Mined Paraphrases
Sławomir Dadas
Sentence embeddings are commonly used in text clustering and semantic retrieval tasks. State-of-the-art sentence representation methods are based on artificial neural networks fine…
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…
Combining neural and knowledge-based approaches to Named Entity Recognition in Polish
Sławomir Dadas
Named entity recognition (NER) is one of the tasks in natural language processing that can greatly benefit from the use of external knowledge sources. We propose a named entity rec…