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We introduce clever, the first curated benchmark for evaluating the generation of specifications and formally verified code in lean With a clever usage of the equivalence between reward models and the corresponding optimal policy, the algorithm features a simple objective that combines (i) a preference optimization loss that directly aligns the policy with human preference, and (ii) a supervised learning loss which explicitly imitates the policy with a baseline distribution. The benchmark comprises of 161 programming problems

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It requires full formal specs and proofs 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

Building on recent explainable ai techniques, this article highlights the pervasiveness of clever hans effects in unsupervised learning and the substantial risks associated with these effects in terms of the prediction accuracy on new data. 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.