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
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The Clever Hans Mirage: A Comprehensive Survey on Spurious. . . Back in the early 20th century, a horse named Hans appeared to perform arithmetic and other intellectual tasks during exhibitions in Germany, while it actually relied solely on involuntary cues in
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
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
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
Submissions | OpenReview Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers Lorenzo Pacchiardi, Marko Tesic, Lucy G Cheke, Jose Hernandez-Orallo 27 Sept 2024 (modified: 05 Feb 2025) Submitted to ICLR 2025 Readers: Everyone
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
LoRA: Low-Rank Adaptation of Large Language Models An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains As we pre-train larger models, full