InFiConD: Interactive No-code Fine-tuning with Concept-based Knowledge Distillation

's interface consists of six coordinated views: (a) The configuration view provides an overview of the dataset and teacher model being distilled, while (b) the student performance view displays a summary of each student model's performance and highlights subsets where student and teacher models misalign. Abstract-- The emergence of large-scale pretrained models has heightened their application in various downstream tasks, yet deployment is a challenge in environments with limited computational resources. Knowledge distillation has emerged as a solution in such scenarios, whereby knowledge from large teacher models is transferred into smaller student' models, but this is a non-trivial process that traditionally requires technical expertise in AI/ML. We develop a novel knowledge distillation pipeline based on extracting text-aligned visual concepts from a concept corpus using multimodal models, and construct highly interpretable linear student models based on visual concepts that mimic a teacher model in a response-based manner. 's interface allows users to interactively fine-tune the student model by manipulating concept influences directly in the user interface. 's human-in-the-loop and visualization-driven approach enables users to effectively create and analyze student models, understand how knowledge is transferred, and efficiently perform fine-tuning operations. We discuss how this work highlights the potential of interactive and visual methods in making knowledge distillation and subsequent no-code fine-tuning more accessible and adaptable to a wider range of users with domain-specific demands. Jinbin Huang and Chris Bryan are with Arizona State Uiversity. Importantly, to serve as new initializations to fine-tune the student model for a few KD has been shown as effective even when the teacher and student epochs, effectively adapting the model based on user instructions. In particular, we are inspired by recent efforts This section provides a brief overview of knowledge distillation, and in KD interpretability that leverage visual concepts---a technique then discusses relevant related work at the intersection of visual analytics originally designed to explain model behaviors [21, 38, 43]. While 2.1 Knowledge Distillation such methods can improve KD interpretability, they primarily rely on Knowledge distillation (KD) [23] is the process of transferring knowledge automated concept extraction pipelines that generate large ensembles of from a large'teacher' PTM to a more compact'student' model.