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.
" 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 ...
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.
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).
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.