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Nothing, but imagine using LLMs for a classification task People out there are so resigned to the models being unreliable that they are really doing things lik
by zenlikethat 16d ago
Nothing, but imagine using LLMs for a classification task
People out there are so resigned to the models being unreliable that they are really doing things like hallucinating deliberately, and then matching the hallucinations to embeddings -
https://softwaredoug.com/blog/2026/08/10/hypothetical-classifications https://softwaredoug.com/blog/2026/08/10/hypothetical-classi...
You could do that or you could just... use a model that will never produce unreliable outputs in the first place.
- threecheese 16d agoBut we're going from "Apple" to "Apple: 99% - trust me". It could still be an image of an orange :)
- zenlikethat 16d agoIt's pretty darn smart. If you did want to hack on it in earnest and find out for yourself, send me an email - nathan@typesafe.ai
- threecheese 15d agoOh I'm on the waiting list, I was just being a pedantic prick. I can't wait to try it.
- deleted 16d ago[deleted]
- nkozyra 16d agoI'm certainly not resigned to that, at least for classification. Even non-frontier models are absurdly good at this in a broad sense. Which would make it hard to judge "a model that will never produce unreliable outputs in the first place" against something that is already really, really good and exceptional in domain-specific areas with the tiniest amount of elbow grease. Speed and cost look good though (for now)!
- ActivePattern 15d agoIt would be great to see benchmarks for Jev that demonstrate the value of calibrated uncertainty. For example, one could set a confidence threshold over which we trust the model decision, and otherwise reject. This provides a lever to trade-off accuracy and automation %. Then we can ask questions like "What % of decisions can we automate to achieve 90% accuracy"?
- suraj_phanindra 14d ago[flagged]