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It requires full formal specs and proofs This ensures that the model remains fast and efficient without losing much accuracy. 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 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 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
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. 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.
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