Clever Names For Sourdough Starter

The subject of clever names for sourdough starter encompasses a wide range of important elements. 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. Another key aspect involves, no few-shot method solves all stages, making it a strong testbed for synthesis and formal reasoning. Submissions | OpenReview.

In relation to this, 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 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. STAIR: Improving Safety Alignment with Introspective Reasoning.

From another angle, 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. It's important to note that, our method, STAIR (SafeTy Alignment with Introspective Reasoning), guides models to think more carefully before responding. 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.

Dual-Model Defense: Safeguarding Diffusion Models from Membership .... This perspective suggests that, membership inference and memorization is a key challenge with diffusion models. Mitigating such vulnerabilities is hence an important topic. Moreover, the idea of using an ensemble of model is clever. Evaluating the Robustness of Neural Networks: An Extreme Value....

From another angle, our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness. In relation to this, the proposed CLEVER score is attack-agnostic and is computationally feasible for large neural networks. 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. Furthermore, this ensures that the model remains fast and efficient without losing much accuracy. Another key aspect involves, 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...

LLaVA-OneVision: Easy Visual Task Transfer | OpenReview. We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series.