Online Training of Large Language Models: Learn while chatting
This addresses the need for more adaptable and user-friendly LLM interactions, though it appears incremental in combining existing concepts like real-time updates and external knowledge.
The paper tackles the problem of inflexibility and lack of persistent learning in interactions between large language models and users, particularly for non-programmers, by introducing an online training paradigm that enables real-time model updates and customization through external interactions.
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.