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LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
May 22, 2024, 4:43 a.m. | James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Duvenaud
cs.LG updates on arXiv.org arxiv.org
Abstract: Machine learning practitioners often face significant challenges in formally integrating their prior knowledge and beliefs into predictive models, limiting the potential for nuanced and context-aware analyses. Moreover, the expertise needed to integrate this prior knowledge into probabilistic modeling typically limits the application of these models to specialists. Our goal is to build a regression model that can process numerical data and make probabilistic predictions at arbitrary locations, guided by natural language text which describes a …
abstract application arxiv challenges context cs.cl cs.lg expertise face knowledge language llm machine machine learning modeling natural natural language numerical potential predictive predictive models prior probabilistic modeling processes stat.ml type
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