Continual Learning and Lifelong Learning in LLMs
In the rapidly evolving landscape of artificial intelligence (AI), continual learning and lifelong learning have emerged as pivotal concepts, particularly in the realm of large language models. As the demand for AI systems capable of adapting to new information and tasks grows, the importance of enabling models to learn continuously throughout their lifespan becomes increasingly evident. In this blog post, we’ll delve into the concepts of continual learning and lifelong learning, exploring their significance in the context of large language models. We’ll also discuss popular techniques used for performing continual learning and lifelong learning, shedding light on how these approaches are shaping the future of AI.
Understanding Continual Learning and Lifelong Learning
Continual learning and lifelong learning are intertwined concepts that emphasize the ability of AI systems to acquire new knowledge and skills over time. Continual learning refers to the process of continuously updating a model’s parameters or architecture to adapt to new tasks or data distributions without catastrophic forgetting of previously learned information. On the other hand, lifelong learning extends this idea further by emphasizing the model’s ability to accumulate knowledge and skills over its entire lifespan, akin to how…
