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It requires full formal specs and proofs Yet, the lack of annotated data and the impact of batch effects, e.g., systematic technical data differences across hospitals, hamper model robustness and generalization We introduce clever, the first curated benchmark for evaluating the generation of specifications and formally verified code in lean
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The benchmark comprises of 161 programming problems Deep learning has led to remarkable advancements in computational histopathology, e.g., in diagnostics, biomarker prediction, and outcome prognosis One common approach is training models to refuse unsafe queries, but this strategy can be vulnerable to clever prompts, often referred to as jailbreak attacks, which can trick the ai into providing harmful responses
Our method, stair (safety alignment with introspective reasoning), guides models to think more carefully before responding.
Our analysis yields a novel robustness metric called clever, which is short for cross lipschitz extreme value for network robustness While, as we mentioned earlier, there can be thorny “clever hans” issues about humans prompting llms, an automated verifier mechanically backprompting the llm doesn’t suffer from these We use a clever technique that involves rotating the data within each layer of the model, making it easier to identify and keep only the most important parts for processing This ensures that the model remains fast and efficient without losing much accuracy.
