I wonder, how do these systems actually learn? What data sources are used, how is it cleaned, and which algorithms come into play? How much data does the learning process require, and how might one need to intervene in this process?
How do natural language models collect and train on data?
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Last year, I tried training my own small LLM prototype for a project—not a 100B parameter model, of course, but it was great for understanding the basic pipeline. The first step was the data: comments scraped from Reddit and some scientific discussions from Twitter. Cleaning it took my entire weekend—special characters, spam, mixed English-Turkish sentences... After filtering through my scripts, I ended up with a 10M sentence dataset. Then came training: Google Colab’s free T4 GPU died in 1-2 days, so I had to continue with a smaller subset.
Next, I copied the first transformer architecture I found by searching "how to train a language model"—naturally, in PyTorch. Everything was going smoothly; the model’s loss dropped from 3.2 to 1.8, but when I paused training to check, it turned out the model was just endlessly repeating sequences in "completion" mode. I stepped in: halved the learning rate, waited another 10k steps, and then added 20% new, high-quality scientific paper abstracts to the dataset. Finally, the model started producing consistent results, especially in text summarization—though on a small scale. From this, I realized that data quality and continuous feedback intervention are the most critical parts of what seems like a simple pipeline.