Automated Machine Learning (AutoML) has simplified complex ML processes such as data pre-processing, model selection, and hyper-parameter searching. However, traditional AutoML frameworks focus solely on discriminative tasks, often falling short in tackling AutoML for generative models. Additionally, these frameworks lack interpretability and user engagement during the training process, primarily due to the absence of human-centered design. It leads to a lack of transparency in final decision-making and limited user control, potentially reducing trust and adoption of AutoML methods. To address these limitations, we introduce UniAutoML, a human-centered AutoML framework that leverages Large Language Models (LLMs) to unify AutoML for both discriminative (e.g., Transformers and CNNs for classification or regression tasks) and generative tasks (e.g., fine-tuning diffusion models or LLMs). The human-centered design of UniAutoML innovatively features a conversational user interface (CUI) that facilitates natural language interactions, providing users with real-time guidance, feedback, and progress updates for better interpretability. This design enhances transparency and user control throughout the AutoML training process, allowing users to seamlessly break down or modify the model being trained. To mitigate potential risks associated with LLM generated content, UniAutoML incorporates a safety guardline that filters inputs and censors outputs. We evaluated UniAutoML's performance and usability through experiments on eight diverse datasets and user studies involving 25 participants, demonstrating that UniAutoML not only enhances performance but also improves user control and trust. Our human-centered design bridges the gap between AutoML capabilities and user understanding, making ML more accessible to a broader audience.

翻译：自动机器学习（AutoML）简化了数据预处理、模型选择和超参数搜索等复杂机器学习流程。然而，传统AutoML框架仅专注于判别式任务，在处理生成式模型的AutoML时往往存在不足。此外，这些框架在训练过程中缺乏可解释性和用户参与度，主要源于以人为本设计的缺失。这导致最终决策缺乏透明度且用户控制有限，可能降低对AutoML方法的信任度和采纳率。为应对这些局限，我们提出UniAutoML——一个基于大型语言模型（LLMs）的以人为本AutoML框架，可统一处理判别式任务（如使用Transformer和CNN进行分类或回归任务）与生成式任务（如微调扩散模型或LLMs）的AutoML流程。UniAutoML的以人为本设计创新性地采用对话式用户界面（CUI），支持自然语言交互，为用户提供实时指导、反馈和进度更新以增强可解释性。该设计提升了整个AutoML训练过程的透明度与用户控制能力，使用户能够无缝拆分或修改正在训练的模型。为降低LLM生成内容可能带来的风险，UniAutoML内置安全防护机制，可过滤输入内容并审核输出结果。我们通过在八个多样化数据集上的实验及包含25名参与者的用户研究，评估了UniAutoML的性能与可用性，结果表明UniAutoML不仅能提升性能，还可增强用户控制力与信任度。我们的以人为本设计弥合了AutoML能力与用户认知之间的鸿沟，使机器学习技术能够惠及更广泛的群体。