Coling-UniA at SciVQA 2025: Few-Shot Example Retrieval and Confidence-Informed Ensembling for Multimodal Large Language Models
This work addresses scientific visual question answering for researchers, but it is incremental as it applies existing methods to a new task with modest improvements.
The paper tackled the SciVQA 2025 Shared Task on Scientific Visual Question Answering by using an ensemble of multimodal large language models with few-shot example retrieval and confidence-based answer selection, achieving an average F1 score of 85.12 and ranking third out of seven systems.
This paper describes our system for the SciVQA 2025 Shared Task on Scientific Visual Question Answering. Our system employs an ensemble of two Multimodal Large Language Models and various few-shot example retrieval strategies. The model and few-shot setting are selected based on the figure and question type. We also select answers based on the models' confidence levels. On the blind test data, our system ranks third out of seven with an average F1 score of 85.12 across ROUGE-1, ROUGE-L, and BERTS. Our code is publicly available.