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.
Abstract (translated)
尽管使用生成式 AI 工具作为特定机器学习应用程序的通用模型具有很大的兴趣，但目前部署的许多区分性 AI 工具的一个关键缺陷是，它们不如生成式 AI 工具（例如 GPT4、Stable Diffusion、Bard 等）具有适应性和用户友好性。这些已经部署的区分性 AI 工具的一个关键不足是，它们不具备像生成式 AI 工具那样的可适应性和易用性，非专家用户可以逐步优化模型输入并实时获得反馈，从而允许用户从开始就建立信任。受到这种新兴协作工作流程的启发，我们开发了一个新的系统架构，使用户能够以与生成式 AI 工具类似的方式与区分性模型（例如用于物体检测、情感分类等）进行合作，并且能轻松地立即提供反馈，并根据需要调整部署的模型。我们的方法对改善这些多功能但传统预测模型的信任、易用性、可扩展性具有影响。
URL
https://arxiv.org/abs/2312.06826
