Clever Learning Minecraft

Understanding clever learning minecraft requires examining multiple perspectives and considerations. CLEVER: A Curated Benchmark for Formally Verified Code Generation. TL;DR: We introduce CLEVER, a hand-curated benchmark for verified code generation in Lean. It requires full formal specs and proofs. No few-shot method solves all stages, making it a strong testbed for synthesis and formal reasoning.

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. Submissions | OpenReview. Promoting openness in scientific communication and the peer-review process 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. Counterfactual Debiasing for Fact Verification.

It's important to note that, 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 relation to this, sTAIR: Improving Safety Alignment with Introspective Reasoning. Moreover, 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. Furthermore, 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. 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.

La RoSA: Enhancing LLM Efficiency via Layerwise Rotated Sparse.... We use a clever technique that involves rotating the data within each layer of the model, making it easier to identify and keep only the most important parts for processing. This ensures that the model remains fast and efficient without losing much accuracy.

Weakly-Supervised Affordance Grounding Guided by Part-Level.... In this work, we focus on the task of weakly supervised affordance grounding, where a model is trained to identify affordance regions on objects using human-object interaction images and egocentric...