Catastrophic Forgetting Scenario

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A Catastrophic Forgetting Scenario is a Neural Network Behavior that involves the complete or substantial forgetting of previously learned information when a neural network is trained on new tasks.



References

2023

  • (Luo et al., 2023) ⇒ Yun Luo, Zhen Yang, Fandong Meng, Yafu Li, Jie Zhou, and Yue Zhang. (2023). “An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning.” arXiv preprint arXiv:2308.08747
    • ABSTRACT: Catastrophic forgetting (CF) is a phenomenon that occurs in machine learning when a model forgets previously learned information as it learns new information. As large language models (LLMs) have shown excellent performance, it is interesting to uncover whether CF exists in the continual fine-tuning of LLMs. In this study, we empirically evaluate the forgetting phenomenon in LLMs' knowledge, from the perspectives of domain knowledge, reasoning, and reading comprehension. The experiments demonstrate that catastrophic forgetting is generally observed in LLMs ranging from 1b to 7b. Furthermore, as the scale increases, the severity of forgetting also intensifies. Comparing the decoder-only model BLOOMZ with the encoder-decoder model mT0, BLOOMZ suffers less forgetting and maintains more knowledge. We also observe that LLMs can mitigate language bias (e.g. gender bias) during continual fine-tuning. Moreover, we find that ALPACA can maintain more knowledge and capacity compared with LLAMA during the continual fine-tuning, which implies that general instruction tuning can help mitigate the forgetting phenomenon of LLMs in the further fine-tuning process.

2018

2017

  • (Kirkpatrick et al., 2017) ⇒ J. Kirkpatrick, R. Pascanu, ... (2017). “Overcoming catastrophic forgetting in neural networks.” In: Proceedings of the National Academy of Sciences. [2]
    • NOTE: It presents methods to overcome catastrophic forgetting, suggesting that it is not an inevitable feature of connectionist models.

1999

  • (French, 1999) ⇒ R.M. French. (1999). “Catastrophic forgetting in connectionist networks.” In: Trends in Cognitive Sciences. [3]
    • NOTE: It discusses the causes, consequences, and various solutions to catastrophic forgetting in neural networks.

1995

  • (Robins, 1995) ⇒ A. Robins. (1995). “Catastrophic forgetting, rehearsal and pseudorehearsal.” In: Connection Science. [4]
    • NOTE: It reviews the problem of catastrophic forgetting and introduces 'sweep rehearsal' as an effective method to minimize it.