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英文字典中文字典相关资料:


  • CLEVER: A Curated Benchmark for Formally Verified Code Generation
    TL;DR: We introduce CLEVER, a hand-curated benchmark for verified code generation in Lean It requires full formal specs and proofs No few-shot method solves all stages, making it a strong testbed for synthesis and formal reasoning
  • Clever: A Curated Benchmark for Formally Verified Code Generation
    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; it evaluates both formal speci-fication generation and implementation synthesis from natural language, requiring formal correctness proofs for both
  • Submissions | OpenReview
    Promoting openness in scientific communication and the peer-review process
  • STAIR: Improving Safety Alignment with Introspective Reasoning
    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
  • Evaluating the Robustness of Neural Networks: An Extreme Value. . .
    Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness The proposed CLEVER score is attack-agnostic and is computationally feasible for large neural networks
  • Counterfactual Debiasing for Fact Verification
    579 In this paper, we have proposed a novel counter- factual framework CLEVER for debiasing fact- checking models Unlike existing works, CLEVER is augmentation-free and mitigates biases on infer- ence stage In CLEVER, the claim-evidence fusion model and the claim-only model are independently trained to capture the corresponding information
  • On the Planning Abilities of Large Language Models : A Critical . . .
    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 tested this setup on a subset of the failed instances in the one-shot natural language prompt configuration using GPT-4, given its larger context window
  • EvoTest: Evolutionary Test-Time Learning for Self-Improving Agentic . . .
    A fundamental limitation of current AI agents is their inability to learn complex skills on the fly at test time, often behaving like “clever but clueless interns” in novel environments This severely limits their practical utility To systematically measure and drive progress on this challenge, we first introduce the Jericho Test-Time Learning (J-TTL) benchmark J-TTL is a new evaluation
  • A Protocol-Driven Platform for Agent-Agnostic Evaluation of LLM Agents
    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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