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Do language models plan ahead for future tokens?
Do transformers "think ahead" during inference at a given position?
Learning Deterministic Finite Automata from Confidence Oracles
Our goal is to learn a DFA representation of the oracle that preserves the information that it is confident in.
Generating Semantic Adversarial Examples with Differentiable Rendering
In this paper we explore semantic adversarial examples (SAEs) where an attacker creates perturbations in the semantic space representing the environment that produces input for the ML model.
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