Imagine an AI that never stops learning, constantly absorbing new information and adapting to changing environments—like a digital version of the human brain. This is the promise of continual learning (CL), a field of AI research that aims to create models capable of learning sequentially from streams of data, just like we do. One of the most exciting applications of CL is in the realm of language models. Think of the large language models (LLMs) that power tools like ChatGPT. These models are typically trained once on a massive dataset and then fine-tuned for specific tasks. But what if they could learn new tasks without forgetting what they already know? That's where continual learning comes in. This research area is exploring how to apply CL principles to LLMs, allowing them to learn new languages, adapt to different writing styles, or even master new domains of knowledge without losing their core abilities. This approach is particularly important for LLMs because retraining them from scratch every time new data becomes available is incredibly resource-intensive. Continual learning offers a more sustainable and efficient way to keep these powerful models up-to-date. However, applying CL to LLMs is not without its challenges. One of the biggest hurdles is 'catastrophic forgetting,' where a model loses previously learned information when trained on new data. Researchers are actively developing strategies to combat this, including techniques like 'experience replay,' where the model revisits past data, and 'parameter isolation,' where different parts of the model are dedicated to different tasks. Another challenge is the sheer size of LLMs. With billions or even trillions of parameters, updating these models efficiently is crucial. Researchers are exploring methods like 'parameter-efficient tuning' to make continual learning feasible for these massive models. The potential benefits of continual learning for LLMs are enormous. Imagine a chatbot that remembers your past conversations and learns your preferences over time, or a translation tool that constantly improves as it encounters new languages and dialects. Continual learning could unlock a new era of AI that is more adaptable, efficient, and ultimately, more human-like in its ability to learn and grow.