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Few-shot continual learning (FSCL) is a challenging setting as it requires models to learn new knowledge with a few examples over time, and fast adapt to new tasks without forgetting previous knowledge. Prompt-tuning, as an efficient learning approach for language models, has shown competitive performance in data-efficient learning for various NLP tasks, motivating us to explore how to effectively perform prompt-tuning in FSCL for text classification. In this work, we focus on studying prompt-tuning for continual classification, aiming to alleviate catastrophic forgetting and improve knowledge transfer with few-shot data in FSCL. After carefully analyzing the limited representation capability of existing soft-prompt initialization methods, we propose Task-Aware Initialization (TAI), a novel initialization approach that can combine the information from both context and label space. Extensive experiments with different language models including recent instruction-finetuned LLM in two FSCL settings (shot-invariant and shot-variant) demonstrate the superiority of TAI over current approaches.
Zhang et al. (Mon,) studied this question.