Large Language Models(LLMs) have dramatically revolutionized the field of
Natural Language Processing(NLP), offering remarkable capabilities that have
garnered widespread usage. However, existing interaction paradigms between LLMs
and users are constrained by either inflexibility, limitations in
customization, or a lack of persistent learning. This inflexibility is
particularly evident as users, especially those without programming skills,
have restricted avenues to enhance or personalize the model. Existing
frameworks further complicate the model training and deployment process due to
their computational inefficiencies and lack of user-friendly interfaces. To
overcome these challenges, this paper introduces a novel interaction
paradigm-'Online Training using External Interactions'-that merges the benefits
of persistent, real-time model updates with the flexibility for individual
customization through external interactions such as AI agents or online/offline
knowledge bases.

大型语言模型 (LLMs) 在自然语言处理 (NLP) 领域产生了巨大的革命，提供了引人注目的功能，得到了广泛的使用。然而，现有的 LLMs 与用户之间的交互范式存在着局限性、定制能力不足或者缺乏持续学习的问题。为了克服这些挑战，本文介绍了一种新的交互范式 -' 使用外部交互进行在线训练 '，通过与 AI 代理或在线 / 离线知识库等外部交互，将持续实时模型更新的好处与个性化定制的灵活性相结合。