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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
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 Our analysis yields a novel robustness metric called clever, which is short for cross lipschitz extreme value for network robustness
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. 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 Hook it up with taskconfig—our handy layer for crafting clever input templates and grabbing outputs steadily via jmespath—and switching agents turns effortless, no extra fiddling needed Our benchmark structure ensures reproducibility by locking in versions.
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.
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