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- 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
- Forum | OpenReview
We introduce ${\\rm C{\\small LEVER}}$, a high-quality, manually curated benchmark of 161 problems for end-to-end verified code generation in Lean Each problem consists of (1) the task of generating
- CLEVER: A Curated Benchmark for Formally Verified Code Generation
This paper introduces CLEVER, a benchmark dataset designed to evaluate LLMs on formally verified code generation It consists of 161 carefully crafted Lean specifications derived from programming problems in the existing HumanEval dataset
- KnowTrace: Explicit Knowledge Tracing for Structured. . .
" This paper introduces a clever incorporation of knowledge graph operation for structured RAG " (Reviewer ifaQ) " The proposed method is straightforward, intuitive, and easy to implement "; " It is innovative that the paper leverages the structured nature of reasoning paths to filter and refine generated trajectories for model training
- 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
- 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)
- Contrastive Learning Via Equivariant Representation - OpenReview
In this paper, we revisit the roles of augmentation strategies and equivariance in improving CL's efficacy We propose CLeVER (Contrastive Learning Via Equivariant Representation), a novel equivariant contrastive learning framework compatible with augmentation strategies of arbitrary complexity for various mainstream CL backbone models
- 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
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