Can better large language models (LLMs) be developed with labeled datasets or with massive datasets consisting of raw text only? Some argue that labeled data is essential for high-quality outputs because it guides the learning process of the models. Others claim that raw text alone is sufficient and that scale (the amount of data) matters more than quality. I've just started diving into this world—what approach do you think is more robust? What factors do you focus on in your development process?
Should datasets for LLMs be labeled or unlabeled?
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The question of whether a dataset should be labeled or unlabeled is actually one of the most fundamental dilemmas in the evolution of LLMs. Back in the day—say, during the supervised learning era—labeled data was non-negotiable; the model was directly told what to learn. But if you look at today’s LLMs, the game has changed entirely because these models operate largely on *unsupervised* or *self-supervised* learning. They consume raw text, uncover sentence structures, and deduce word relationships all on their own.
So does that mean labels are obsolete? Not so fast. Labeled data is still crucial, especially during the *fine-tuning* phase. Want to train a chatbot to mimic a specific conversational style? Good luck doing that with just raw text—it’s nearly impossible. But when building a brand-new language model from scratch, the standard approach is to pre-train on *massive* amounts of unlabeled text first, then fine-tune with a smaller, high-quality labeled dataset. OpenAI’s GPT series, for instance, followed this exact blueprint.
I think the key here is to separate two things when choosing a dataset: 1) the *pre-training* phase (large volumes of unlabeled text) and 2) the *fine-tuning* phase (smaller, high-quality labeled data). Instead of asking whether scale or quality matters more, it’s smarter to ask: *Which phase requires which type of data?* For a general-purpose chatbot, unlabeled text alone might suffice, but if you need a specialized assistant for a niche domain, fine-tuning with labeled data is a must.
It really depends on the use case, but in general, I focus on the *"I don’t know the exact amount"* approach when working with LLMs. For specific tasks like classification, summarization, or Q&A where precision is key, I blend small labeled datasets with pretraining on large-scale text. For example, I fine-tuned a BERT model on a medical domain using just 5K labeled examples, which still accounted for only 5% of the final training data—and it worked well.
What’s worked best for me is starting with a massive unlabeled corpus (e.g., scraped data or Common Crawl) for basic pretraining, then adding fine-tuning with task-specific labeled examples. In time-sensitive projects, I prioritize sheer volume (e.g., training on 100M unlabeled tokens) and later apply instruction tuning with well-designed prompts. The key is not to obsess over data perfection upfront—scaling often compensates for early errors if you later refine with relevant data.