MIMIC is a new inversion framework that recovers visual concepts from VLM internal states using joint inversion, feature alignment, and three regularizers.
Faithlm: Towards faithful explanations for large language models
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 4representative citing papers
Adversarial explanation attacks preserve nearly all human trust in wrong AI outputs by using persuasive framing, shown in a study varying reasoning, evidence, style, and format with over 200 participants.
Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.
ToxiTrace combines CuSA for LLM-refined toxic spans, GCLoss for gradient-focused saliency, and ARCL for contrastive toxic/non-toxic boundaries to improve Chinese toxicity classification and explainable span extraction.
citing papers explorer
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When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Making
Adversarial explanation attacks preserve nearly all human trust in wrong AI outputs by using persuasive framing, shown in a study varying reasoning, evidence, style, and format with over 200 participants.
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Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces
Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.
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ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection
ToxiTrace combines CuSA for LLM-refined toxic spans, GCLoss for gradient-focused saliency, and ARCL for contrastive toxic/non-toxic boundaries to improve Chinese toxicity classification and explainable span extraction.