As I delve into the world of Large Language Models (LLMs), I’m struck by the prevalence of two approaches: fine-tuning and prompting. From competitions to projects, it seems like most solutions boil down to either fine-tuning a base model or crafting strong prompts. Even tasks that start out as ‘generalization to unseen examples’ – like zero-shot classification – often end up framed as prompting problems in practice.
But I’m left wondering: is there more to leveraging LLMs than these two strategies? Are there other practical ways to improve zero-shot performance without simply becoming a better prompt? I’d love to hear from practitioners who’ve explored directions beyond the usual fine-tune/prompt spectrum.
One potential avenue could be exploring alternative training objectives or regularization techniques that encourage more robust generalization. Another might be using LLMs as feature extractors or few-shot learners, rather than solely relying on fine-tuning or prompting. Perhaps there are even ways to use LLMs in tandem with other machine learning models to create more powerful hybrid systems.
While fine-tuning and prompting are undoubtedly powerful tools, I believe there’s still much to be discovered in the realm of LLMs. By exploring beyond these familiar approaches, we may uncover new techniques that unlock even more impressive capabilities from these powerful models.