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This paper introduces PersuasionTrace, a framework for studying multi-turn persuasion in human-LLM interaction, using a Bayesian-network simulated target that models belief updates. The framework reveals that LLMs are persuasive across topics and modalities, and that the Bayesian target better matches human belief dynamics than vanilla LLM simulators.
Ψ-Bench is a benchmark for evaluating LLMs' ability to influence users through persuasive dialogues, incorporating user profiles for personalized persuasion. Experiments show that even state-of-the-art models have room for improvement, and access to client profiles significantly boosts performance.
This paper proposes a taxonomy of eight temporal frames for news discourse, presents a multilingual dataset with expert annotations, and evaluates supervised and zero-shot classification for detecting temporal framing.
A discussion on how knowledge work revolves around persuasion, featuring a quote from Dwarkesh Patel about the conflation of intelligence and power.
This paper uses large language models to analyze persuasion dynamics and polarization in Reddit's r/ChangeMyView, finding that empathetic alignment increases belief change while frontal refutation diminishes it.