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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
- EVALUATING THE ROBUSTNESS OF NEURAL NET : A E VALUE THEORY APPROACH
te the CLEVER scores for the same set of images and attack targets To the best of our knowledge, CLEVER is the first attack-independent robustness score that is capable of handling the large networks studied in this paper, so we directly r `2 and `1 norms, and Figure 4 visualizes the results for `1 norm Similarly, Table 2 comp
- Leaving the barn door open for Clever Hans: Simple features predict. . .
The integrity of AI benchmarks is fundamental to accurately assess the capabilities of AI systems The internal validity of these benchmarks - i e , making sure they are free from confounding
- Ignore Previous Prompt: Attack Techniques For Language Models
Abstract Transformer-based large language models (LLMs) provide a powerful foundation for natural language tasks in large-scale customer-facing applications However, studies that explore their vulnerabilities emerging from malicious user interac-tion are scarce By proposing PROMPTINJECT, a prosaic alignment framework for mask-based iterative adversarial prompt composition, we examine how GPT
- 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
- Initialization using Update Approximation is a Silver Bullet for. . .
TL;DR: We provably optimally approximate full fine-tuning in low-rank subspaces throughout the entire training process using a clever initialization scheme, achieving significant gains in parameter efficiency
- The Pitfalls of Next-Token Prediction - OpenReview
This verifies our hypothesis that the Clever Hans cheat absorbs away supervision that is critical to learn the first token At the end of this section, we provide more intuition for how the absence of Clever Hans cheat, allows the teacherless models to solve this task that language has enough redundancy to be conducive for next-token prediction
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