Paper-Conference

Utilising Large Language Models for Adversarial Attacks in Text-to-SQL: A Perpetrator and Victim Approach
This paper investigates the use of Large Language Models (LLMs) for the Text-to-SQL task, both as Perpetrator models for generating …
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dfkinit2b at CheckThat! 2025: Leveraging LLMs and Ensemble of Methods for Multilingual Claim Normalization
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PolBiX: Detecting LLMs' Political Bias in Fact-Checking through X-phemisms
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Signals of Strain: Using Biosignals to Assess Fatigue in Video Conferencing
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XplaiNLP at CheckThat! 2025: Multilingual Subjectivity Detection with Finetuned Transformers and Prompt-Based Inference with Large Language Models
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CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI Systems
Conversational explainable artificial intelligence (ConvXAI) systems based on large language models (LLMs) have garnered significant …
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Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem
Natural language explanations (NLEs) are vital for elucidating the reasoning behind large language model (LLM) decisions. Many …
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Disentangling User States in QoE: Situation-Dependent and Independent Factors
While current Quality of Experience (QoE) formation models recognize the impact of user states on perception, they often overlook the …
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Evaluating Human-Centered AI Explanations: Introduction of an XAI Evaluation Framework for Fact-Checking
The rapidly increasing amount of online information and the ad-vent of Generative Artificial Intelligence (GenAI) make the …
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Implications of Regulations on Large Generative AI Models in the Super-Election Year and the Impact on Disinformation
With the rise of Large Generative AI Models (LGAIMs), disinformation online has become more concerning than ever before. Within the …
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