面向自动持续学习：一种用于持续指令调优的自适应框架
Towards Automatic Continual Learning: A Self-Adaptive Framework for Continual Instruction Tuning
AI总结：
针对现有持续指令调优方法侧重旧知识保留、忽视新知识选择及领域部署难题，提出带代理模型动态过滤的自适应框架，可降本提效并实现自主更新。
摘要：
持续指令调优使大语言模型（LLM）能够在保留过往知识的同时进行增量学习，然而现有方法主要聚焦于如何保留旧知识，而非选择需要学习的新知识。在特定领域场景中，维持数据质量与管理系统约束仍是核心挑战。为解决这些问题，我们提出一种自动化持续指令调优框架，该框架可动态过滤输入数据，识别并减少连续更新中的冗余数据。我们的方法利用小型代理模型开展高效的基于困惑度的过滤，并更新代理模型以确保过滤标准与部署模型的演进状态保持一致。与现有静态数据选择方法相比，我们的框架可有效处理增量获取的数据与分布偏移问题。此外，它通过支持无缝模型更新、版本回滚及自动检查点评估，解决了实际部署中的挑战。我们在真实医疗场景中对该系统进行了评估，它将计算成本降低了66.7%，提升了模型性能，并实现了自主更新，证明了其在自动持续指令调优方面的有效性。
英文摘要：
Continual instruction tuning enables large language models (LLMs) to learn incrementally while retaining past knowledge, whereas existing methods primarily focus on how to retain old knowledge rather than on selecting which new knowledge to learn. In domain-specific contexts, maintaining data quality and managing system constraints remain key challenges. To address these issues, we propose an automated continual instruction tuning framework that dynamically filters incoming data, which identify and reduce redundant data across successive updates. Our approach utilizes a small proxy model for efficient perplexity-based filtering, and updates the proxy to ensure that the filtering criteria remain aligned with the evolving state of the deployed model. Compared to existing static data selection methods, our framework can effectively handle incrementally acquired data and shifting distributions. Additionally, it addresses practical deployment challenges by enabling seamless model updates, supporting version rollback and incorporating automatic checkpoint evaluation. We evaluated the system in real-world medical scenarios. It reduced computational costs by 66.7% and improved model performance, and achieved autonomous updates, thus demonstrating its effectiveness for automatic continual instruction tuning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
