Language models exhibit inherent security vulnerabilities, which may be related to several factors, among them the malicious alteration of the input data. Such weaknesses compromise the robustness of language models, which is more critical when adversarial attacks are stealthy and do not require high computational resources. In this work, we study how vulnerable English language models are to adversarial attacks based on subtle modifications of the input of pretrained English language models. We claim that the attack may be more effective if it is targeted to the most salient words for the discriminative task of the language models. Accordingly, we propose a new attack built upon a two-step approach: first, we use a posteriori explainability methods to identify the most influential words for the classification task, and second, we replace them with contextual synonyms retrieved by a small language model. Since the attack has to be as stealthy as possible, we also propose a new evaluation measure that combines the effectiveness of the attack with the number of modifications performed. The results show that pretrained English language models are vulnerable to minimal semantic changes, which makes the design of countermeasure methods imperative.

We introduce the findings and results of SemEval-2025 Task 8: Question Answering over Tabular Data. We featured two subtasks, DataBench and DataBench Lite. DataBench consists on question answering over tabular data, and DataBench Lite small comprising small datasets that might be easier to manage by current models by for example fitting them into a prompt. The task was open for any approach, but their answer has to conform to a required typing format. In this paper we present the task, analyze a number of system submissions and discuss the results. The results show how approaches leveraging LLMs dominated the task, with larger models exhibiting a considerably superior performance compared to small models.