BoKA: Bayesian Optimization based Knowledge Amalgamation for Multi-unknown-domain Text Classification

With breakthroughs in pretrained language models, a large number of finetuned models specialized in distinct domains have surfaced online. Yet, when faced with a fresh dataset covering multiple (sub)domains, their performance might degrade. Reusing these available finetuned models to train a new model is a more feasible solution than the finetuning method that demands extensive manual labeling. Knowledge Amalgamation (KA) is such a model reusing technique, which derives a new model (termed student model) by amalgamating those trained models (termed teacher models) tailored for distinct domains, bypassing the need for manual labeling. However, when the domains of text samples are unknown, selecting a number of appropriate teacher models (simply called a combination) for reuse becomes complicated. To learn an accurate student model, the classical KA method resorts to manual selections, a process both tedious and inefficient. Our study pioneers the automation of this combination selection process for KA in the fundamental text classification task, an area previously unexplored.