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In this work, we describe our experiments and participating systems in theBioASQ Task 9b Phase B challenge of biomedical question answering. We havefocused on finding the ideal answers and investigated multi-task fine-tuningand gradual unfreezing techniques on transformer-based language models. Forfactoid questions, our ALBERT-based systems ranked first in test batch 1 andfourth in test batch 2. Our DistilBERT systems outperformed the ALBERT variantsin test batches 4 and 5 despite having 81% fewer parameters than ALBERT.However, we observed that gradual unfreezing had no significant impact on themodel's accuracy compared to standard fine-tuning.

Transformer-based Language Models for Factoid Question Answering at BioASQ9b