Abstract: When a learning system is deployed, the data it encounters may no longer resemble its training data. The nature of this shift is typically unknown, whether due to changing conditions, adversarial manipulation, or simply a new deployment context. Without assumptions on the shift, reliable prediction is in general impossible, as the test distribution may bear no resemblance to the training data. This talk studies a simple but powerful response: allow the learner to abstain from prediction when it lacks…

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Abstract: The emergence of large language models has prompted a surge of interest into theoretical models that might give us insight into both their successes and their shortcomings. We'll give an overview of recent work in this direction, focusing on a surprising line of positive results that shows it is possible to give guarantees for language-generation algorithms even in the absence of any probabilistic assumptions, in a framework known as "language generation in the limit". These results suggest interesting notions…