Empirical study reporting 98% base-model attribution accuracy and cross-encoder fingerprinting of unseen system prompts (AUC 0.768 single-conversation, 0.943 with 50 conversations) in black-box LLM agents.
Ai generated text detection using instruction fine-tuned large language and transformer-based models
2 Pith papers cite this work. Polarity classification is still indexing.
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Shared task findings show near-perfect binary detection of AI-generated text but greater difficulty in attributing outputs to particular language models.
citing papers explorer
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Black-Box Forensics for Conversational LLM Agents
Empirical study reporting 98% base-model attribution accuracy and cross-encoder fingerprinting of unseen system prompts (AUC 0.768 single-conversation, 0.943 with 50 conversations) in black-box LLM agents.
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Findings of the Counter Turing Test: AI-Generated Text Detection
Shared task findings show near-perfect binary detection of AI-generated text but greater difficulty in attributing outputs to particular language models.