A remaining advantage of large language models is that as they get larger, they tend to hallucinate less, simply because the odds of the training set containing a desired answer improve with size. If a solid "I don't know" detector is developed for inference, then you can try a small language model first.

An implication is that successful research in "I don't know" detection could destroy hundreds of billions in shareholder value.

>> A remaining advantage of large language models is that as they get larger, they tend to hallucinate less

First time I hear that...not really true.

"Understanding Why Language Models Hallucinate: Testing Reasoning Against Priors" - https://arxiv.org/abs/2607.00447

"Calibrated Language Models Must Hallucinate" - https://arxiv.org/abs/2311.14648

"TruthfulQA: Measuring How Models Mimic Human Falsehoods" - https://arxiv.org/abs/2109.07958

Another "cool but we don't know how yet" thing would be a "confidence interval" so we know how much to trust LLM responses. Or while we're fantasizing, they could just know everything all the time regardless of training data. The "if a solid" part is easy to imagine, hard to implement :)