2 citations · 3 across the 4 of their papers we have counts for
4 papers
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.…
Silver Retriever: Advancing Neural Passage Retrieval for Polish Question Answering
Piotr Rybak, Maciej Ogrodniczuk
Modern open-domain question answering systems often rely on accurate and efficient retrieval components to find passages containing the facts necessary to answer the question. Rece…
PolQA: Polish Question Answering Dataset
Piotr Rybak, Piotr Przybyła, Maciej Ogrodniczuk
Recently proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. However, data annotation is kn…
Keyword Extraction from Short Texts with a Text-To-Text Transfer Transformer
Piotr Pęzik, Agnieszka Mikołajczyk-Bareła, Adam Wawrzyński +2
The paper explores the relevance of the Text-To-Text Transfer Transformer language model (T5) for Polish (plT5) to the task of intrinsic and extrinsic keyword extraction from short…