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。

大规模语言模型的在线训练：聊天中学习

Online Training of Large Language Models: Learn while chatting

Synaptic memory consolidation has been heralded as one of the key mechanisms
for supporting continual learning in neuromorphic Artificial Intelligence (AI)
systems. Here we report that a Fowler-Nordheim (FN) quantum-tunneling device
can implement synaptic memory consolidation similar to what can be achieved by
algorithmic consolidation models like the cascade and the elastic weight
consolidation (EWC) models. The proposed FN-synapse not only stores the
synaptic weight but also stores the synapse's historical usage statistic on the
device itself. We also show that the operation of the FN-synapse is
near-optimal in terms of the synaptic lifetime and we demonstrate that a
network comprising FN-synapses outperforms a comparable EWC network for a small
benchmark continual learning task. With an energy footprint of femtojoules per
synaptic update, we believe that the proposed FN-synapse provides an
ultra-energy-efficient approach for implementing both synaptic memory
consolidation and persistent learning.

用 Fowler-Nordheim 量子隧穿器件实现突触记忆巩固并存储其历史使用统计信息，这提供了一种超低能耗的方法来实现突触记忆巩固和持久学习。