Language-Induced Priors from LLMs guide source selection in cold-start domain adaptation through an EM algorithm, matching oracle MSE under a correct prior and remaining asymptotically consistent.
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Formalizes preference learning from a no-regret or Boltzmann-converging learner with theoretical guarantees or impossibility results for IRL algorithms.
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Language-Induced Priors for Domain Adaptation
Language-Induced Priors from LLMs guide source selection in cold-start domain adaptation through an EM algorithm, matching oracle MSE under a correct prior and remaining asymptotically consistent.
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Learning the Preferences of a Learning Agent
Formalizes preference learning from a no-regret or Boltzmann-converging learner with theoretical guarantees or impossibility results for IRL algorithms.