3 papers
cs.CL2025
Contextual Breach: Assessing the Robustness of Transformer-based QA Models
Asir Saadat, Nahian Ibn Asad
Contextual question-answering models are susceptible to adversarial perturbations to input context, commonly observed in real-world scenarios. These adversarial noises are designed…
cs.CV2025
VisionTrap: Unanswerable Questions On Visual Data
Asir Saadat, Syem Aziz, Shahriar Mahmud +2
Visual Question Answering (VQA) has been a widely studied topic, with extensive research focusing on how VLMs respond to answerable questions based on real-world images. However, t…
cs.CL2024
When Not to Answer: Evaluating Prompts on GPT Models for Effective Abstention in Unanswerable Math Word Problems
Asir Saadat, Tasmia Binte Sogir, Md Taukir Azam Chowdhury +1
Large language models (LLMs) are increasingly relied upon to solve complex mathematical word problems. However, being susceptible to hallucination, they may generate inaccurate res…