Large language models (LLMs) are used for long-form question answering (LFQA), which
requires them to generate paragraph-length answers to complex questions. While LFQA has …

SK-QuAD is the first manually annotated dataset of questions and answers in Slovak. It
consists of more than 91k factual questions and answers from various fields. Each question …

This paper showcases PoQuAD—a SQuAD-like contribution to building Question Answering
tools for Polish. It largely follows the usual Machine Reading Comprehension format, but a …

We present BenCzechMark (BCM), the first comprehensive Czech language benchmark
designed for large language models, offering diverse tasks, multiple task formats, and …

This paper presents the efforts towards creating PoQuAD, a dataset for training automatic
question answering models in Polish. It justifies why having native data is vital for training …

Pre-trained models for Czech Natural Language Processing are often evaluated on purely
linguistic tasks (POS tagging, parsing, NER) and relatively simple classification tasks such …

Question answering (QA) of non-mainstream languages requires specific adaptations of the
current methods tested primarily with very large English resources. In this paper, we present …

This thesis explores a new language model called ALBERT, released by Google Research
in 2019. The ALBERT architecture has been very successful in English NLP tasks, and this …

This thesis describes an answer selection module optimized for the Czech language. The
module is being developed for open-domain question answering system Automatic …

Open domain question answering now inevitably builds upon advanced neural models
processing large unstructured textual sources serving as a kind of underlying knowledge …