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We use CLEVER to evaluate several state-of-the-art LLMs prompted in a few-shot manner and show that they can only solve up to end-to-end verified code generation 1/161 problem, establishing CLEVER as a challenging frontier benchmark for program synthesis and formal reasoning. In summary, our contributions include: 1.
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.
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.