19 citations · 20 across the 5 of their papers we have counts for
5 papers
Artificial Conversations, Real Results: Fostering Language Detection with Synthetic Data
Fatemeh Mohammadi, Tommaso Romano, Samira Maghool +1
Collecting high-quality training data is essential for fine-tuning Large Language Models (LLMs). However, acquiring such data is often costly and time-consuming, especially for non…
Enhancing Model Fairness and Accuracy with Similarity Networks: A Methodological Approach
Samira Maghool, Paolo Ceravolo
In this paper, we propose an innovative approach to thoroughly explore dataset features that introduce bias in downstream machine-learning tasks. Depending on the data format, we u…
Are Large Language Models the New Interface for Data Pipelines?
Sylvio Barbon Junior, Paolo Ceravolo, Sven Groppe +5
A Language Model is a term that encompasses various types of models designed to understand and generate human communication. Large Language Models (LLMs) have gained significant at…
Modeling and predicting students' engagement behaviors using mixture Markov models
R. Maqsood, P. Ceravolo, C. Romero +1
Students' engagements reflect their level of involvement in an ongoing learning process which can be estimated through their interactions with a computer-based learning or assessme…
Subgroup Discovery in MOOCs: A Big Data Application for Describing Different Types of Learners
J. M. Luna, H. M. Fardoun, F. Padillo +2
The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery approach based on MapReduce.…