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LLMs can learn to manipulate their own internal activations, but this control is limited and often mimicked by external generation.
ERRLESS reveals that incorporating Bayesian principles into symbolic regression can yield more interpretable and accurate models, outperforming traditional methods in uncertainty quantification.
Adversarial training on just 66 carefully chosen statements can achieve near-zero attack success rates, revolutionizing safety alignment in language models.