Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI

Elron Bandel, Yotam Perlitz, Elad Venezian, Roni Friedman, Ofir Arviv, Matan Orbach, Shachar Don-Yehiya, Dafna Sheinwald, Ariel Gera, Leshem Choshen, Michal Shmueli-Scheuer, Yoav Katz

Abstract
In the dynamic landscape of generative NLP, traditional text processing pipelines limit research flexibility and reproducibility, as they are tailored to specific dataset, task, and model combinations. The escalating complexity, involving system prompts, model-specific formats, instructions, and more, calls for a shift to a structured, modular, and customizable solution.Addressing this need, we present Unitxt, an innovative library for customizable textual data preparation and evaluation tailored to generative language models. Unitxt natively integrates with common libraries like HuggingFace and LM-eval-harness and deconstructs processing flows into modular components, enabling easy customization and sharing between practitioners. These components encompass model-specific formats, task prompts, and many other comprehensive dataset processing definitions. The Unitxt Catalog centralizes these components, fostering collaboration and exploration in modern textual data workflows. Beyond being a tool, Unitxt is a community-driven platform, empowering users to build, share, and advance their pipelines collaboratively. Join the Unitxt community at https://github.com/IBM/unitxt
Anthology ID:
2024.naacl-demo.21
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kai-Wei Chang, Annie Lee, Nazneen Rajani
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
207–215
Language:
URL:
https://aclanthology.org/2024.naacl-demo.21
DOI:
10.18653/v1/2024.naacl-demo.21
Bibkey:
Cite (ACL):
Elron Bandel, Yotam Perlitz, Elad Venezian, Roni Friedman, Ofir Arviv, Matan Orbach, Shachar Don-Yehiya, Dafna Sheinwald, Ariel Gera, Leshem Choshen, Michal Shmueli-Scheuer, and Yoav Katz. 2024. Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations), pages 207–215, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI (Bandel et al., NAACL 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-demo.21.pdf
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