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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.06136 |
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| _version_ | 1866917983808389120 |
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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 |