REALISTA optimizes continuous combinations of valid editing directions in latent space to produce realistic adversarial prompts that elicit hallucinations more effectively than prior methods, including on large reasoning models.
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Clotho ranks LLM test inputs by failure likelihood using pre-generation hidden states and GMMs, achieving 0.716 ROC-AUC after labeling 5.4% of inputs on average across eight tasks and three models, with transfer to proprietary models.
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REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations
REALISTA optimizes continuous combinations of valid editing directions in latent space to produce realistic adversarial prompts that elicit hallucinations more effectively than prior methods, including on large reasoning models.
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Clotho: Measuring Task-Specific Pre-Generation Test Adequacy for LLM Inputs
Clotho ranks LLM test inputs by failure likelihood using pre-generation hidden states and GMMs, achieving 0.716 ROC-AUC after labeling 5.4% of inputs on average across eight tasks and three models, with transfer to proprietary models.