AutoXAI frameworks automatically select and optimize explainability methods using quantitative metrics such as faithfulness and plausibility. But do these metrics align with human perception of explanation quality? We investigate this question for attribution-based explanation methods, which assign token-level relevance scores to the input, in the context of claim matching, a core fact-checking task in which a bi-encoder model matches social media posts to previously fact-checked claims. Combining a large-scale evaluation of 11 XAI methods with a controlled user study, we find that methods recommended by AutoXAI metrics are perceived by users as most helpful for understanding the claim-post match, while deprioritized methods are judged as misleading. We further show that simple post-processing of explanations can improve human-perceived usefulness without degrading faithfulness. Because AutoXAI recommends the top-ranked method in practice, this alignment at the decisive top-versus-bottom boundary positions these metrics as reliable indicators for distinguishing clearly strong from clearly weak methods.

The proliferation of transformer-based language models has revolutionized NLP domain while simultaneously introduced significant challenges regarding model transparency and trustworthiness. The complexity of achieving explainable systems in this domain is evidenced by the extensive array of explanation methods and evaluation metrics developed by researchers. To address the challenge of selecting optimal explainability approaches, we present o-mega, a hyperparameter optimization tool designed to automatically identify the most effective explainable AI methods and their configurations within the semantic matching domain. We evaluate o-mega on a post-claim matching pipeline using a curated dataset of social media posts paired with refuting claims. Our tool systematically explores different explainable methods and their hyperparameters, demonstrating improved transparency in automated fact-checking systems. As a result, such automated optimization of explanation methods can significantly enhance the interpretability of claim-matching models in critical applications such as misinformation detection, contributing to more trustworthy and transparent AI systems.

In recent years, we can observe a trend of creating various specific language models (LMs) within the Slavic language family with the Bert architecture. However, with an increasing number of parameters of LM, a larger amount of text is required for good performance, which can hinder the development and creation of LMs for specific languages. Our research is looking for a solution in Curriculum learning(CL) methods that can help us build better models with a lower amount of text in comparison with current LMs, which can help in better prtraining of models with low resource languages(LRL). Therefore, we replicate the BabyLM Challenge in the Slovak language (Dataset: https://huggingface.co/datasets/ubokri/SlovakBabyLM, Code: https://github.com/baucek/Slovakbabylm/tree/main). Additionally, apply CL to test and see the difference in the application of CL methods on the English and Slovak languages and evaluate whether the CL improves performance of LM. Our experiments show that the use of CL methods as preprocessing methods is significant for improving model performance in sentiment analysis and question answering.