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Grounded in real-world evidence, GPS-Bench reveals that fine-tuning LLMs on historical policy data dramatically enhances the accuracy of multi-agent simulations in predicting policy outcomes.
Adversarial training on just 66 carefully chosen statements can achieve near-zero attack success rates, revolutionizing safety alignment in language models.
