LLMs like GPT are pre-trained and cannot continue learning in their current form. It is infeasible to retrain gigantic models from scratch every few months. We must develop strategies to overcome this limitation and enable continuous learning in language models.
Fine-tuning: Though not exactly tantamount to continuous learning, one can fine-tune an LLM on new data or tasks. Fine-tuning involves training the model for a few more epochs on a smaller, task-specific dataset. This allows the model to adapt its knowledge to the new task or data and improve its performance.
Elastic Weight Consolidation (EWC): EWC is a technique to mitigate catastrophic forgetting in neural networks. It adds a regularization term to the loss function that penalizes changes to the most important model parameters during fine-tuning. This helps preserve the knowledge acquired during pre-training while learning new information.
Incremental learning: Incremental learning algorithms, such as Gradient Episodic Memory (GEM), store a small subset of the previous experiences and use them alongside new data during the learning process. This approach helps maintain knowledge from previous tasks while learning new information.
Meta-learning: Meta-learning focuses on training models to learn how to learn. These models can quickly adapt to new tasks and data using a small number of training examples. One popular approach is Model-Agnostic Meta-Learning (MAML), which optimizes model parameters such that they can be fine-tuned with minimal updates for new tasks.
Modular architectures: Developing AI systems with modular architectures, where different components or sub-networks can be trained independently or in parallel, can enable continuous learning. This approach reduces interference between tasks and allows the model to expand its capabilities without the need for complete retraining.
Neural network plasticity: Implementing mechanisms inspired by the plasticity of biological neural networks, such as Hebbian learning or neuromodulation, can help AI systems adapt their connection weights or learning rates in response to new experiences, enabling continuous learning.
Lifelong learning algorithms: Developing lifelong learning algorithms, such as Progress & Compress, which involves learning a new task and then compressing the acquired knowledge into a compact form to minimize interference with subsequent tasks. This approach aims to strike a balance between retaining old knowledge and acquiring new information.
Memory-augmented neural networks: Integrating external memory modules, such as Differentiable Neural Computers (DNCs) or Neural Turing Machines (NTMs), into the model architecture can enable the AI system to store and retrieve information over longer timescales, which could facilitate continuous learning.