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Papers & Research
Language-Induced Priors (LIP) uses LLMs to translate natural-language descriptions into probabilistic priors that guide learning in cold-start settings. The framework identifies which historical systems are most relevant based on semantic context, then automatically adapts the new model using EM. Tested on descriptive, predictive, and prescriptive (RL) models.