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  • 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
    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
  • 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
  • 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
  • D4: Improving LLM Pretraining via Document De-Duplication and. . .
    Our results indicate that clever data selection can significantly improve LLM pre-training, calls into question the common practice of training for a single epoch on as much data as possible, and demonstrates a path to keep improving our models past the limits of randomly sampling web data
  • Right on Time: Revising Time Series Models by Constraining their . . .
    The reliability of deep time series models is often compromised by their tendency to rely on confounding factors, which may lead to incorrect outputs Our newly recorded, naturally confounded dataset named P2S from a real mechanical production line emphasizes this To avoid “Clever-Hans” moments in time series, i e , to mitigate confounders, we introduce the method Right on Time (RioT
  • SDFR: Synthetic Data for Face Recognition Competition
    The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data The submissions were evaluated and ranked on a diverse set of seven benchmarking datasets
  • Learnable Representative Coefficient Image Denoiser for. . .
    Fully characterizing the spatial-spectral priors of hyperspectral images (HSIs) is crucial for HSI denoising tasks Recently, HSI denoising models based on representative coefficient images (RCIs) under the spectral low-rank decomposition framework have garnered significant attention due to their clever utilization of spatial-spectral information in HSI at a low cost However, current methods
  • Leaving the barn door open for Clever Hans: Simple features predict. . .
    This paper focuses on exploring the "Clever Hans" effect, also known as the "shortcut learning" effect, in which the trained model exploits simple and superficial correlations instead of intended capabilities to solve the evaluation tasks





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