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

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Bibliographische Detailangaben
Hauptverfasser:	Moses, Movina, Elkaref, Mohab, Barry, James, Tanaka, Shinnosuke, Kuruvanthodi, Vishnudev, Herr, Nathan, Watson, Campbell D, De Mel, Geeth
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Computation and Language Artificial Intelligence
Online-Zugang:	Acceder al recurso
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author	Moses, Movina Elkaref, Mohab Barry, James Tanaka, Shinnosuke Kuruvanthodi, Vishnudev Herr, Nathan Watson, Campbell D De Mel, Geeth
author_facet	Moses, Movina Elkaref, Mohab Barry, James Tanaka, Shinnosuke Kuruvanthodi, Vishnudev Herr, Nathan Watson, Campbell D De Mel, Geeth
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2504_06136
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	QGen Studio: An Adaptive Question-Answer Generation, Training and Evaluation Platform Moses, Movina Elkaref, Mohab Barry, James Tanaka, Shinnosuke Kuruvanthodi, Vishnudev Herr, Nathan Watson, Campbell D De Mel, Geeth Computation and Language Artificial Intelligence 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.
title	QGen Studio: An Adaptive Question-Answer Generation, Training and Evaluation Platform
topic	Computation and Language Artificial Intelligence
url	https://arxiv.org/abs/2504.06136

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