Catastrophic Forgetting
Catastrophic forgetting describes the phenomenon where artificial neural networks, upon learning new tasks, lose previously acquired knowledge. Current research focuses on mitigating this issue through various strategies, including parameter-efficient fine-tuning methods (like LoRA), generative model-based data replay, and novel optimization algorithms that constrain gradient updates or leverage hierarchical task structures. Addressing catastrophic forgetting is crucial for developing robust and adaptable AI systems capable of continuous learning in real-world applications, particularly in domains like medical imaging, robotics, and natural language processing where data streams are constantly evolving.
Papers
Sequential Editing for Lifelong Training of Speech Recognition Models
Devang Kulshreshtha, Saket Dingliwal, Brady Houston, Nikolaos Pappas, Srikanth Ronanki
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting
Tianhao Li, Shangjie Li, Binbin Xie, Deyi Xiong, Baosong Yang
Unlocking Continual Learning Abilities in Language Models
Wenyu Du, Shuang Cheng, Tongxu Luo, Zihan Qiu, Zeyu Huang, Ka Chun Cheung, Reynold Cheng, Jie Fu