User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models

12 Pages Posted: 9 Jan 2024

See all articles by Son The Nguyen

Son The Nguyen

University of Illinois at Chicago - Department of Information and Decision Sciences

Theja Tulabandhula

University of Illinois at Chicago

Mary Beth Watson-Manheim

University of Illinois at Chicago

Date Written: December 11, 2023

Abstract

While there is significant interest in using generative AI tools as general-purpose models for specific ML applications, discriminative models are much more widely deployed currently. One of the key shortcomings of these discriminative AI tools that have been already deployed is that they are not adaptable and user-friendly compared to generative AI tools (e.g., GPT4, Stable Diffusion, Bard, etc.), where a non-expert user can iteratively refine model inputs and give real-time feedback that can be accounted for immediately, allowing users to build trust from the start. Inspired by this emerging collaborative workflow, we develop a new system architecture that enables users to work with discriminative models (such as for object detection, sentiment classification, etc.) in a fashion similar to generative AI tools, where they can easily provide immediate feedback as well as adapt the deployed models as desired. Our approach has implications on improving trust, user-friendliness, and adaptability of these versatile but traditional prediction models.

Keywords: Discriminative and Generative AI, ML workflow, ML systems, MLOps

Suggested Citation

Nguyen, Son The and Tulabandhula, Theja and Watson-Manheim, Mary Beth, User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models (December 11, 2023). Available at SSRN: https://ssrn.com/abstract=4662955 or http://dx.doi.org/10.2139/ssrn.4662955

Son The Nguyen (Contact Author)

University of Illinois at Chicago - Department of Information and Decision Sciences ( email )

University Hall, Room 2404, M/C 294
Chicago, IL 60607-7124
United States

Theja Tulabandhula

University of Illinois at Chicago ( email )

1200 W Harrison St
Chicago, IL 60607
United States

Mary Beth Watson-Manheim

University of Illinois at Chicago ( email )

1200 W Harrison St
Chicago, IL 60607
United States

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