Class-Incremental Learning based on Label Generation (2023)

Abstract: Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) setting due to catastrophic forgetting (CF). This paper reports our finding that if we formulate CIL as a continual label generation problem, CF is drastically reduced and the generalizable representations of pre-trained models can be better retained. We thus propose a new CIL method (VAG) that also leverages the sparsity of vocabulary to focus the generation and creates pseudo-replay samples by using label semantics. Experimental results show that VAG outperforms baselines by a large margin.

## introduction large pre-trained language models (plms) have become the de facto standard in building nlp systems. however, how to best use them for continual learning (cl) is still a significant question @xcite @xcite . many existing studies focus on task-incremental learning (til) where the model learns distinct tasks sequentially and is given the task identity for inference. these works usually keep the plm unchanged and update a series of additional structures such as adapters @xcite or prompts @xcite . though effective, these methods cannot be used in a more challenging setting of class-incremental learning (cil) which does not provide task information at test time. cil aims to build a single model to make predictions over incrementally learned classes organized as tasks (formal definition in §2). @xcite conducted a comparative study on plm in cl and showed that plms perform extremely poorly in the 1 our code is publicly available at https://github.com/ shaoyijia/vag . ## vag system we present the proposed vag system which reframes cil as a continual label generation problem. figure 3 gives an overview of vag with two major components. ## conclusion we presented the vag system which solves cil based on label generation. we showed that migrating to the generation framework gives a drastic performance boost and eases the representation collapse of the pre-trained model. experimental results demonstrate the effectiveness of vag.