QGen Studio: An Adaptive Question-Answer Generation, Training and Evaluation Platform

Authors

  • Movina Moses IBM Research
  • Mohab Elkaref IBM Research Europe
  • James Barry IBM Research Europe
  • Shinnosuke Tanaka IBM Research Europe
  • Vishnudev Kuruvanthodi IBM Research Europe
  • Nathan Herr University College London
  • Campbell D Watson IBM Research
  • Geeth De Mel IBM Research Europe

DOI:

https://doi.org/10.1609/aaai.v39i28.35362

Abstract

We present QGen Studio: an adaptive question-answer generation, training, and evaluation platform. QGen Studio enables users to leverage large language models (LLMs) to create custom question-answer datasets and fine-tune models on this synthetic data. It features a dataset viewer and model explorer to streamline this process. The dataset viewer provides key metrics and visualizes the context from which the QA pairs are generated, offering insights into data quality. The model explorer supports model comparison, allowing users to contrast the performance of their trained LLMs against other models, supporting performance benchmarking and refinement. QGen Studio delivers an interactive, end-to-end solution for generating QA datasets and training scalable, domain-adaptable models. The studio will be open-sourced soon, allowing users to deploy it locally.

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Published

2025-04-11

How to Cite

Moses, M., Elkaref, M., Barry, J., Tanaka, S., Kuruvanthodi, V., Herr, N., Watson, C. D., & Mel, G. D. (2025). QGen Studio: An Adaptive Question-Answer Generation, Training and Evaluation Platform. Proceedings of the AAAI Conference on Artificial Intelligence, 39(28), 29670-29672. https://doi.org/10.1609/aaai.v39i28.35362