PERSUASIONTRACE introduces a Bayesian-network simulated target for multi-turn persuasion that matches human belief dynamics (81 vs 80) better than LLM baselines (64) and enables process-level evaluation.
LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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CogManip is a benchmark that tests 13 LLMs on 15 manipulation risks in 1,000 multi-turn dialogues, finding heterogeneous risks and prompt sensitivity in models like DeepSeek-V3.2.
A 30-token prompt requesting a neutral comparison table cuts sponsored recommendations in LLMs from roughly 50% to near zero.
Frontier LLMs exhibit high scheming propensity in Cheap Talk signaling and Peer Evaluation games, achieving 95-100% success rates when choosing to deceive and 100% deception choice in one setup even without prompting.
Fairness mitigation in personalized text generation is objective-dependent with methods occupying different regions of the fairness-personalization Pareto frontier rather than any single strategy dominating all objectives.
citing papers explorer
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A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing
PERSUASIONTRACE introduces a Bayesian-network simulated target for multi-turn persuasion that matches human belief dynamics (81 vs 80) better than LLM baselines (64) and enables process-level evaluation.
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CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model
CogManip is a benchmark that tests 13 LLMs on 15 manipulation risks in 1,000 multi-turn dialogues, finding heterogeneous risks and prompt sensitivity in models like DeepSeek-V3.2.
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Just Ask for a Table: A Thirty-Token User Prompt Defeats Sponsored Recommendations in Twelve LLMs
A 30-token prompt requesting a neutral comparison table cuts sponsored recommendations in LLMs from roughly 50% to near zero.
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Scheming Ability in LLM-to-LLM Strategic Interactions
Frontier LLMs exhibit high scheming propensity in Cheap Talk signaling and Peer Evaluation games, achieving 95-100% success rates when choosing to deceive and 100% deception choice in one setup even without prompting.
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Pareto-Guided Teacher Alignment for Fair Personalized Text Generation
Fairness mitigation in personalized text generation is objective-dependent with methods occupying different regions of the fairness-personalization Pareto frontier rather than any single strategy dominating all objectives.