Papers with Catastrophic forgetting
Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System (2022.naacl-industry)
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Tao Zhu, Zhe Zhao, Weijie Liu, Jiachi Liu, Yiren Chen, Weiquan Mao, Haoyan Liu, Kunbo Ding, Yudong Li, Xuefeng Yang
| Challenge: | Existing continual learning methods use data replay, parameter isolation and regularization to mitigate catastrophic forgetting. |
| Approach: | They propose a parameter-efficient continual learning framework that updates parameters offline and then trains using an online regularization method. |
| Outcome: | The proposed framework reduces catastrophic forgetting and saves the model with the changed parameters instead of all parameters. |
Exploring Forgetting in Large Language Model Pre-Training (2025.acl-long)
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| Challenge: | Existing research on task-level forgetting in LLMs has focused on pretraining . but, there is limited attention to finer-grained forgetting during training . |
| Approach: | They investigated the existence and measurement of forgetting in pre-training . they examined low-cost, straightforward methods to mitigate forgetting during the pre- training phase . |
| Outcome: | The proposed methods could be used to mitigate forgetting during the pre-training phase and offer insights into the dynamics of forgetting. |
Continual Learning Long Short Term Memory (2020.findings-emnlp)
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| Challenge: | Existing approaches to prevent catastrophic forgetting in neural networks are based on the stability-plasticity dilemma, but only a limited size of old data is available. |
| Approach: | They propose a Continual Learning Long Short Term Memory cell in Recurrent Neural Network (RNN) that considers the state of each individual task's output gates and the correlation of the states between tasks. |
| Outcome: | The proposed method significantly improves on spoken language understanding tasks over state-of-the-art approaches. |