告诉我该学习什么：将神经记忆通用化以在自然语言中可控
Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language
AI总结：
本文提出了一种基于自然语言指令的通用神经记忆系统，实现对学习内容的灵活控制，适用于医疗和客户服务等需要动态更新的场景。
摘要：
现代机器学习模型被部署在多样且非平稳的环境中，它们必须持续适应新任务和演变的知识。持续微调和上下文学习成本高且脆弱，而神经记忆方法承诺轻量级更新和最小遗忘。然而，现有神经记忆模型通常假设单一固定目标和同质信息流，使用户无法控制模型在时间上记住或忽略什么。为了解决这一挑战，我们提出了一种通用的神经记忆系统，根据在自然语言中指定的学习指令进行灵活更新。我们的方法使适应性代理能够从异质信息源选择性学习，支持如医疗保健和客户服务等场景，其中固定目标的记忆更新不足。
英文摘要：
Modern machine learning models are deployed in diverse, non-stationary environments where they must continually adapt to new tasks and evolving knowledge. Continual fine-tuning and in-context learning are costly and brittle, whereas neural memory methods promise lightweight updates with minimal forgetting. However, existing neural memory models typically assume a single fixed objective and homogeneous information streams, leaving users with no control over what the model remembers or ignores over time. To address this challenge, we propose a generalized neural memory system that performs flexible updates based on learning instructions specified in natural language. Our approach enables adaptive agents to learn selectively from heterogeneous information sources, supporting settings, such as healthcare and customer service, where fixed-objective memory updates are insufficient.
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。
