How to manage catastrophic forgetting in LLMs?

Catastrophic forgetting, also known as catastrophic interference, is a significant challenge in training Large Language Models (LLMs). It refers to the phenomenon where a model forgets previously learned information upon learning new information. This issue is particularly problematic in continual learning settings where the model needs to learn from a stream of data while retaining past knowledge. Here are some strategies to manage catastrophic forgetting in LLMs, backed by reliable and recognized sources, along with examples.

1. Regularization Techniques:
- Elastic Weight Consolidation (EWC): This method penalizes changes to the important weights of the neural network. It computes a Fisher Information matrix to determine the importance of each parameter and applies a regularization term that keeps significant weights close to their original values. Researchers Kirkpatrick et al. (2017) highlighted its effectiveness in mitigating forgetting in their paper titled “Overcoming catastrophic forgetting in neural networks” (PNAS).

Example: Suppose we initially train an LLM on a corpus of scientific articles. When we later train it on a collection of fiction books, EWC ensures that the weights crucial for understanding scientific texts do not deviate too much during the subsequent training.

1. Replay Methods:
- Experience Replay (ER): This involves storing a subset of the original training data and mixing it with new data during the training process. The model periodically revisits the stored data to reinforce old knowledge. Rebuffi et al., in their work “iCaRL: Incremental Classifier and Representation Learning” (CVPR, 2017), demonstrated the utility of replay methods in image classification.

Example: For an LLM, we could maintain a buffer of key sentences or paragraphs from initial training datasets. When introducing new data, these samples would be included in the mini-batches to ensure the model revisits and reinforces previously learned content.

1. Generative Replay:
- Instead of storing raw data, the model generates pseudo-data from its learned distribution to simulate past experiences. Shin et al. (2017) put forward this idea in their paper “Continual Learning with Deep Generative Replay” (NeurIPS).

Example: If our LLM learns about current events, it can be programmed to generate summaries of past events it learned before. During new training sessions, these generated summaries would be used to self-reinforce older information.

1. Architectural Methods:
- Progressive Neural Networks: This approach involves keeping existing neural networks fixed and training new networks that leverage the earlier ones. This method, as shown by Rusu et al. (“Progressive Neural Networks,” arXiv preprint arXiv:1606.04671, 2016), facilitates the retention of past knowledge while acquiring new skills.

Example: When training an LLM initially on English and later extending it to learn French, progressive neural networks would lock down the architecture learned for English and add new layers or modules for French. These modules would still access the representations learned by the English network, ensuring knowledge transfer without interference.

1. Hybrid Models:
- Combining various techniques can offer a robust solution. For example, a hybrid approach may employ EWC together with replay methods to reinforce old knowledge actively and prevent significant weight changes.

Example: When building an LLM that learns sequentially from literature, science, and history domains, a hybrid model would use EWC to stabilize weights critical to each domain and replay methods to revisit exemplars from all domains during training.

In conclusion, managing catastrophic forgetting in LLMs involves a multifaceted approach encompassing regularization techniques like EWC, replay methods, generative models, architectural adjustments, and hybrid solutions. These strategies, grounded in rigorous research, help maintain the balance between integrating new information and retaining previously learned knowledge.

Sources:
- Kirkpatrick, J., et al. (2017). “Overcoming catastrophic forgetting in neural networks.” Proceedings of the National Academy of Sciences.
- Rebuffi, S.-A., et al. (2017). “iCaRL: Incremental Classifier and Representation Learning.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
- Shin, H., et al. (2017). “Continual Learning with Deep Generative Replay.” NeurIPS.
- Rusu, A. A., et al. (2016). “Progressive Neural Networks.” arXiv preprint arXiv:1606.04671.