Unsupervised Elicitation of Language Models

Researchers are exploring ways to get AI language models to generate useful outputs without relying on heavily curated or labeled datasets—essentially, letting the models "figure it out" on their own. The discussion around this approach, sparked by a Hacker News thread, digs into whether unsupervised methods can match or even outperform traditional supervised training. Some are optimistic, while others caution about unpredictable quirks or biases that might emerge when models aren't steered by human-labeled data.