Effectively and efficiently adapting a pre-trained language model (PLM) for
human-centered text understanding (HCTU) is challenging since user tokens are
million-level in most personalized applications and do not have concrete
explicit semantics. A standard and parameter-efficient approach (e.g., LoRA)
necessitates memorizing numerous suits of adapters for each user. In this work,
we introduce a personalized LoRA (PLoRA) with a plug-and-play (PnP) framework
for the HCTU task. PLoRA is effective, parameter-efficient, and dynamically
deploying in PLMs. Moreover, a personalized dropout and a mutual information
maximizing strategies are adopted and hence the proposed PLoRA can be well
adapted to few/zero-shot learning scenarios for the cold-start issue.
Experiments conducted on four benchmark datasets show that the proposed method
outperforms existing methods in full/few/zero-shot learning scenarios for the
HCTU task, even though it has fewer trainable parameters. For reproducibility,
the code for this paper is available at: this https URL

提出了一种个性化的 LoRA（PLoRA）方法，采用插拔框架，通过个性化的适应、自适应的 dropout 和最大化互信息的策略，解决了个性化应用中用户标记数量多、语义模糊等问题，从而更好地适应于冷启动情况，并在人文化文本理解任务的全面 / 少量 / 零样本学习场景中超越了现有方法。