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英文字典中文字典相关资料:


  • 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
  • Forum - OpenReview
    Promoting openness in scientific communication and the peer-review process
  • 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
  • The Clever Hans Mirage: A Comprehensive Survey on Spurious. . .
    This survey on spurious correlations uses the Clever Hans metaphor to motivate the problem, formalizes a group-based setup g=(y,a) with core metrics (worst-group, average-group, bias-conflicting), and explains why models latch onto shortcuts (simplicity bias, training dynamics)
  • 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
  • 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
  • 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
  • Sparse but Critical: A Token-Level Analysis of Distributional. . .
    Main claims of the paper are supported, but the execution of experiments might be significantly strenghtened further (see weaknesses) S2 Experiments with cross-sampling and advantage reweighting are interesting and clever and seem to be a promising analysis toolkit; however, see W3 and W4
  • Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign . . .
    The experimental design is very strong and well-thought-out Using a synthetic reachability task effectively isolates reasoning capabilities from world knowledge, which is a major confounder in other evaluations The operationalization of deception into Deceptive Intention (ρ) and Deceptive Behavior (δ) is both clever and principled





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