VLSP 2025 MLQA-TSR Challenge: Vietnamese Multimodal Legal Question Answering on Traffic Sign Regulation
It provides a benchmark dataset for building intelligent systems in multimodal legal domains, specifically for traffic sign regulation in Vietnam, which is incremental as it extends existing tasks to a new language and domain.
This paper introduced the VLSP 2025 MLQA-TSR challenge, tackling Vietnamese multimodal legal question answering on traffic sign regulation, with best results of 64.55% F2 score for retrieval and 86.30% accuracy for question answering.
This paper presents the VLSP 2025 MLQA-TSR - the multimodal legal question answering on traffic sign regulation shared task at VLSP 2025. VLSP 2025 MLQA-TSR comprises two subtasks: multimodal legal retrieval and multimodal question answering. The goal is to advance research on Vietnamese multimodal legal text processing and to provide a benchmark dataset for building and evaluating intelligent systems in multimodal legal domains, with a focus on traffic sign regulation in Vietnam. The best-reported results on VLSP 2025 MLQA-TSR are an F2 score of 64.55% for multimodal legal retrieval and an accuracy of 86.30% for multimodal question answering.