针对大型多式联机模型的持续指令调
概括
大型多式联机模型 (LMM) 在持续的指令调过程中面临灾难性的遗忘. 这项研究适应了持续学习方法,表明它们可以减轻遗忘并改善LMM在新的视觉语言任务上的表现.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 指令调整通过多任务联合训练使大型多式联运模型 (LMM) 与人类意图保持一致.
- 对所有新出现的视觉语言任务进行全面的培训是不切实际的,需要更有效的方法,如持续学习.
研究的目的:
- 调查在连续指令调过程中LMM中发生的灾难性遗忘.
- 评估现有的持续学习方法对LMM指令调的适用性.
- 在LMMs中开发有效的持续学习的新策略.
主要方法:
- 建立了LMM持续指令调整的第一个基准.
- 整合和调整传统的持续学习方法.
- 探索任务相似性动态,以告知规范化和模型扩展.
主要成果:
- 在持续指令调下,在LMM中确认了灾难性遗忘.
- 证明了传统的持续学习方法的不同有效性.
- 通过使用任务相似性告知策略,展示了改进的模型性能.
结论:
- 持续学习对于适应LMMs以应对不断变化的视觉语言任务至关重要.
- 现有的持续学习方法可以适应,但特定任务的策略是有益的.
- 利用任务相似性的新方法有效地减轻遗忘并增强LMM的能力.
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