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Evasive steganographic payloads in LLMs can be detected again by strategically recontextualizing the data, even after successful evasion of traditional methods.
Escape the greedy trap: Convex optimization yields tokenizers that compress better and come with optimality guarantees.
Achieve 70% language identification accuracy with just five labeled samples per language using a novel tokenization-based approach.
Post-training LLMs is more about finding the right "key" (a few kilobytes of parameters) to unlock pre-existing knowledge than learning new information.
