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We introduce clever, the first curated benchmark for evaluating the generation of specifications and formally verified code in lean This ensures that the model remains fast and efficient without losing much accuracy. The benchmark comprises of 161 programming problems
Our analysis yields a novel robustness metric called clever, which is short for cross lipschitz extreme value for network robustness 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 It requires full formal specs and proofs
En prediction objectives for basic graph navigation tasks
This demonstrates that while transformers can 116 represent world states for mazes, they ma 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. We are largely inspired by recent advances on foundation models and the unparalleled generalisation ability.
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 Leaving the barn door open for clever hans 05 feb 2025) submitted to iclr 2025 readers
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