science1 distinct publisher
A Gaussian-process objective turns an LLM's overconfidence into a calibrated error bar
GOLLuM fits a language model and a Gaussian process under a single probabilistic objective, so the model learns from being confidently wrong. Its authors report matching classical Bayesian optimisation on over 40 per cent fewer experiments.
Publishers:nature.com
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