The Clever Hans Mirage: A Comprehensive Survey on Spurious. . . 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)
Withdraw - OpenReview Review: The paper is a theoretical analysis of adversarial robustness of Graph Laplacian based semi-supervised learning Pros: The paper addresses an important problem of adversarial robustness by exploiting unlabeled data It studies the theoretical robustness of graph Laplacian approach Cons: The attack model is not very well motivated and connected to some realistic situations The
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
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
Forum - OpenReview Promoting openness in scientific communication and the peer-review process
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 interaction are scarce By proposing PROMPTINJECT, a prosaic alignment framework for mask-based iterative adversarial prompt composition, we examine how GPT
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
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