57 citations · 295 across the 30 of their papers we have counts for
31 papers
From Latent to Observable Position-Based Click Models in Carousel Interfaces
Santiago de Leon-Martinez, Robert Moro, Branislav Kveton +1
Click models are a central component of learning and evaluation in recommender systems, yet most existing models are designed for single ranked list interfaces. In contrast, modern…
Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification
Branislav Pecher, Jan Cegin, Robert Belanec +3
Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, making them promising tools in both high- and low-resource languages. One particularly valuable…
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…
Riding the Carousel: The First Extensive Eye Tracking Analysis of Browsing Behavior in Carousel Recommenders
Santiago de Leon-Martinez, Robert Moro, Branislav Kveton +1
Carousels have become the de-facto standard user interface in online services. However, there is a lack of research in carousels, particularly examining how recommender systems may…
RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces
Santiago de Leon-Martinez, Jingwei Kang, Robert Moro +4
Carousel interfaces are widely used in e-commerce and streaming services, but little research has been devoted to them. Previous studies of interfaces for presenting search and rec…
Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification
Jan Cegin, Branislav Pecher, Jakub Simko +3
The generative large language models (LLMs) are increasingly used for data augmentation tasks, where text samples are paraphrased (or generated anew) and then used for classifier f…
A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Models
Ivan Srba, Olesya Razuvayevskaya, João A. Leite +10
In the age of social media and generative AI, the ability to automatically assess the credibility of online content has become increasingly critical, complementing traditional appr…
Task Prompt Vectors: Effective Initialization through Multi-Task Soft-Prompt Transfer
Robert Belanec, Simon Ostermann, Ivan Srba +1
Prompt tuning is an efficient solution for training large language models (LLMs). However, current soft-prompt-based methods often sacrifice multi-task modularity, requiring the tr…
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy Interpolation
Branislav Pecher, Jan Cegin, Robert Belanec +3
While fine-tuning of pre-trained language models generally helps to overcome the lack of labelled training samples, it also displays model performance instability. This instability…
Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance
Branislav Pecher, Ivan Srba, Maria Bielikova
When solving NLP tasks with limited labelled data, researchers typically either use a general large language model without further update, or use a small number of labelled samples…
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…
Automatic Combination of Sample Selection Strategies for Few-Shot Learning
Branislav Pecher, Ivan Srba, Maria Bielikova +1
In few-shot learning, the selection of samples has a significant impact on the performance of the model. While effective sample selection strategies are well-established in supervi…
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…
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness
Branislav Pecher, Ivan Srba, Maria Bielikova
Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-learning or few-shot learning, aims to effectively train a model using only a small a…
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…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis
Santiago de Leon-Martinez, Robert Moro, Maria Bielikova
Eye tracking in recommender systems can provide an additional source of implicit feedback, while helping to evaluate other sources of feedback. In this study, we use eye tracking d…
Multilingual Previously Fact-Checked Claim Retrieval
Matúš Pikuliak, Ivan Srba, Robert Moro +7
Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the c…
Searching for Discriminative Words in Multidimensional Continuous Feature Space
Marius Sajgalik, Michal Barla, Maria Bielikova
Word feature vectors have been proven to improve many NLP tasks. With recent advances in unsupervised learning of these feature vectors, it became possible to train it with much mo…
Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systems
Andrea Hrckova, Robert Moro, Ivan Srba +2
To mitigate the negative effects of false information more effectively, the development of Artificial Intelligence (AI) systems to assist fact-checkers is needed. Nevertheless, the…
Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles
Ivan Srba, Robert Moro, Matus Tomlein +8
In this paper, we present results of an auditing study performed over YouTube aimed at investigating how fast a user can get into a misinformation filter bubble, but also what it t…
Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims
Ivan Srba, Branislav Pecher, Matus Tomlein +4
False information has a significant negative influence on individuals as well as on the whole society. Especially in the current COVID-19 era, we witness an unprecedented growth of…