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Off-path robustness of LLMs can be dramatically influenced by model size and alignment, revealing vulnerabilities often masked by standard evaluations.
Standard LLM sampling methods unintentionally censor low-frequency words, turning Shakespeare into corporate speak.
Fine-tuning unlocks LLMs' surprising ability to predict how memorable a sentence is and how long it takes to read, exceeding traditional methods.
