FraudBench shows that current multimodal LLMs and specialized AI-image detectors often fail to spot AI-generated fake damage in refund evidence, with true positive rates frequently below 50% on synthetic subsets while producing false positives on real damage.
Promptception: How Sensitive Are Large Multimodal Models to Prompts?
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
PEEM is a multi-criteria LLM-based evaluator for prompts and responses that aligns with standard accuracy while enabling zero-shot prompt optimization via feedback.
citing papers explorer
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FraudBench: A Multimodal Benchmark for Detecting AI-Generated Fraudulent Refund Evidence
FraudBench shows that current multimodal LLMs and specialized AI-image detectors often fail to spot AI-generated fake damage in refund evidence, with true positive rates frequently below 50% on synthetic subsets while producing false positives on real damage.
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PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses
PEEM is a multi-criteria LLM-based evaluator for prompts and responses that aligns with standard accuracy while enabling zero-shot prompt optimization via feedback.