PREF-XAI treats explanations as ranked alternatives and learns additive utility functions from limited user feedback to select and discover personalized rule explanations for black-box models.
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RUBEN discovers minimal rule sets explaining RAG LLM outputs via novel pruning and applies them to evaluate LLM safety against adversarial injections.
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PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models
PREF-XAI treats explanations as ranked alternatives and learns additive utility functions from limited user feedback to select and discover personalized rule explanations for black-box models.
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RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems
RUBEN discovers minimal rule sets explaining RAG LLM outputs via novel pruning and applies them to evaluate LLM safety against adversarial injections.