o-MEGA: Optimized Methods for Explanation Generation and Analysis

• URL: http://arxiv.org/abs/2510.00288v1
• Date: Tue, 30 Sep 2025 21:08:36 GMT
• Title: o-MEGA: Optimized Methods for Explanation Generation and Analysis
• Authors: Ľuboš Kriš, Jaroslav Kopčan, Qiwei Peng, Andrej Ridzik, Marcel Veselý, Martin Tamajka,
• Abstract summary: textbftexttto-mega is a tool designed to automatically identify the most effective explainable AI methods.<n>We demonstrate improved transparency in automated fact-checking systems.
• Score: 1.4113638641649384
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 \textbf{\texttt{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.

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