Browsing by Author "Arisoy, E."

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Thumbnail Image
    Conference Object
    Evaluating Large Language Models in Data Generation for Low-Resource Scenarios: A Case Study on Question Answering
    (International Speech Communication Association, 2025) Arisoy, E.; Menevşe, M.U.; Manav, Y.; Özgür, A.
    Large Language Models (LLMs) are powerful tools for generating synthetic data, offering a promising solution to data scarcity in low-resource scenarios. This study evaluates the effectiveness of LLMs in generating question-answer pairs to enhance the performance of question answering (QA) models trained with limited annotated data. While synthetic data generation has been widely explored for text-based QA, its impact on spoken QA remains underexplored. We specifically investigate the role of LLM-generated data in improving spoken QA models, showing performance gains across both text-based and spoken QA tasks. Experimental results on subsets of the SQuAD, Spoken SQuAD, and a Turkish spoken QA dataset demonstrate significant relative F1 score improvements of 7.8%, 7.0%, and 2.7%, respectively, over models trained solely on restricted human-annotated data. Furthermore, our findings highlight the robustness of LLM-generated data in spoken QA settings, even in the presence of noise. © 2025 International Speech Communication Association. All rights reserved.