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

Movina Moses, Mohab Elkaref, James Barry, Shinnosuke Tanaka, Vishnudev Kuruvanthodi +3 more
4/8/2025
cs.CLcs.AI

No official code found. Best implementation: IBM/qgen-studio (11 stars, Apache-2.0, last commit 2025-09-18).

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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Code Implementations(1)

IBM/qgen-studioCommunity65%
Apache-2.0

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

110SCSS, PythonApr 9, 20251 years agoApache-2.0

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Cite this paper

@article{moses2025qgen,
  title  = {QGen Studio: An Adaptive Question-Answer Generation, Training and Evaluation Platform},
  author = {Movina Moses and Mohab Elkaref and James Barry and Shinnosuke Tanaka and Vishnudev Kuruvanthodi and Nathan Herr and Campbell D Watson and Geeth De Mel},
  year   = {2025},
  eprint = {2504.06136},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.06136v1}
}

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