Continual learning has gained increasing importance as it facilitates the
acquisition and refinement of scalable knowledge and skills in language models.
However, existing methods typically encounter strict limitations and challenges
in real-world scenarios, such as reliance on experience replay, optimization
constraints, and inference task-ID. In this study, we introduce the Scalable
Language Model (SLM) to overcome these limitations within a more challenging
and generalized setting, representing a significant advancement toward
practical applications for continual learning. Specifically, we propose the
Joint Adaptive Re-Parameterization (JARe), integrated with Dynamic Task-related
Knowledge Retrieval (DTKR), to enable adaptive adjustment of language models
based on specific downstream tasks. This approach leverages the task
distribution within the vector space, aiming to achieve a smooth and effortless
continual learning process. Our method demonstrates state-of-the-art
performance on diverse backbones and benchmarks, achieving effective continual
learning in both full-set and few-shot scenarios with minimal forgetting.
Moreover, while prior research primarily focused on a single task type such as
classification, our study goes beyond, with the large language model, i.e.,
LLaMA-2, to explore the effects across diverse domains and task types, such
that a single language model can be decently scaled to broader applications.

该研究引入了可扩展语言模型（SLM）来解决现有方法在实际应用中遇到的限制和挑战，通过联合自适应重参数化（JARe）和动态任务相关知识检索（DTKR），实现了语言模型的自适应调节，从而在广泛的应用领域中实现了有效的持续学习。