Presents a game-theoretic model with group actions for data augmentation in LLM adversarial evaluation, demonstrating local generalization from fine-tuning on three model families and redefining benchmarks as orbits under group actions.
Why llm safety guardrails collapse after fine-tuning: A similarity analysis between alignment and fine-tuning datasets
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Benign fine-tuning drifts LLM parameters toward danger directions; SQSD scores each sample by the projection difference of its induced update onto safety versus danger vectors.
Semantically invariant row and column permutations in tables can cause LLMs to output incorrect answers, and a gradient-based attack called ATP efficiently finds such permutations that degrade performance across many models.
citing papers explorer
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The Evaluation Game: Beyond Static LLM Benchmarking
Presents a game-theoretic model with group actions for data augmentation in LLM adversarial evaluation, demonstrating local generalization from fine-tuning on three model families and redefining benchmarks as orbits under group actions.
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From Parameter Dynamics to Risk Scoring : Quantifying Sample-Level Safety Degradation in LLM Fine-tuning
Benign fine-tuning drifts LLM parameters toward danger directions; SQSD scores each sample by the projection difference of its induced update onto safety versus danger vectors.
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The Power of Order: Fooling LLMs with Adversarial Table Permutations
Semantically invariant row and column permutations in tables can cause LLMs to output incorrect answers, and a gradient-based attack called ATP efficiently finds such permutations that degrade performance across many models.