27 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Brno University of TechnologyCZ7 papers
- Al-Mustaqbal University1 paper
- Alsalam University CollegeIQ1 paper
- Baghdad Medical CityIQ1 paper
- European School of MaterialsDE1 paper
- German Research Centre for Artificial IntelligenceDE1 paper
- Kempelen Institute of Intelligent Technologies1 paper
- MIT Lincoln LaboratoryUS1 paper
- Pennsylvania State UniversityUS1 paper
- University of BabylonIQ1 paper
- University of PittsburghUS1 paper
9 papers
PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models
Robert Belanec, Ivan Srba, Maria Bielikova
Parameter-Efficient Fine-Tuning (PEFT) methods address the increasing size of Large Language Models (LLMs). Currently, many newly introduced PEFT methods are challenging to replica…
PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark
Robert Belanec, Branislav Pecher, Ivan Srba +1
Despite the state-of-the-art performance of Large Language Models (LLMs) achieved on many tasks, their massive scale often leads to high computational and environmental costs, limi…
Soft Language Prompts for Language Transfer
Ivan Vykopal, Simon Ostermann, Marián Šimko
Cross-lingual knowledge transfer, especially between high- and low-resource languages, remains challenging in natural language processing (NLP). This study offers insights for impr…
Comparing the physical characteristics of ultrasound and magnetic resonance imaging to diagnose ovarian cysts
Tariq Nadhim Jassim, Rasha Tahseen Ibrahim, Mohsen Hamoud Jasim +3
Background, For the purpose of determining the appropriate course of therapy to maintain fertility, a correct diagnosis of ovarian cysts is crucial. Objective, To contrast the resu…
On Sensitivity of Learning with Limited Labelled Data to the Effects of Randomness: Impact of Interactions and Systematic Choices
Branislav Pecher, Ivan Srba, Maria Bielikova
While learning with limited labelled data can improve performance when the labels are lacking, it is also sensitive to the effects of uncontrolled randomness introduced by so-calle…
Authorship Obfuscation in Multilingual Machine-Generated Text Detection
Dominik Macko, Robert Moro, Adaku Uchendu +7
High-quality text generation capability of recent Large Language Models (LLMs) causes concerns about their misuse (e.g., in massive generation/spread of disinformation). Machine-ge…
Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation
Jan Cegin, Branislav Pecher, Jakub Simko +3
The latest generative large language models (LLMs) have found their application in data augmentation tasks, where small numbers of text samples are LLM-paraphrased and then used to…
Disinformation Capabilities of Large Language Models
Ivan Vykopal, Matúš Pikuliak, Ivan Srba +3
Automated disinformation generation is often listed as an important risk associated with large language models (LLMs). The theoretical ability to flood the information space with d…
KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection
Timo Hromadka, Timotej Smolen, Tomas Remis +2
This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of t…