5 papers
ASafe: Counterfactual Evidence-Aligned Adaptive Agent Collaboration for Safe and Effective Visual Question Answering
Quanxing Xu, Ling Zhou, Xian Zhong +4
Visual Question Answering (VQA) with Multimodal Large Language Models (MLLMs) requires not only producing safe and effective responses, but also grounding safety decisions in the m…
MetaRA: Metamorphic Robustness Assessment for Multimodal Large Language Model-based Visual Question Answering Systems
Quanxing Xu, Yuhao Tian, Ling Zhou +4
Visual Question Answering (VQA), as the representative multimodal task, serves as a key benchmark for evaluating the reasoning capabilities of Multimodal Large Language Models (MLL…
Enhancing Visual Question Answering with Multimodal LLMs via Chain-of-Question Guided Retrieval-Augmented Generation
Quanxing Xu, Ling Zhou, Xian Zhong +3
With advances in multimodal research and deep learning, Multimodal Large Language Models (MLLMs) have emerged as a powerful paradigm for a wide range of multimodal tasks. As a core…
OAD-Promoter: Enhancing Zero-shot VQA using Large Language Models with Object Attribute Description
Quanxing Xu, Ling Zhou, Feifei Zhang +2
Large Language Models (LLMs) have become a crucial tool in Visual Question Answering (VQA) for handling knowledge-intensive questions in few-shot or zero-shot scenarios. However, t…
Exploring Individual Factors in the Adoption of LLMs for Specific Software Engineering Purposes
Stefano Lambiase, Gemma Catolino, Fabio Palomba +2
Context: The advent of Large Language Models (LLMs) is transforming software development, significantly enhancing software engineering (SE) processes. Research has explored their r…