Benchmarking Factual Robustness of LLMs via Multi-conversation Persuasion

arXiv:2609.16777 · cs.CL · Submitted 2026-09-15 · Read on arXiv

cs.CL

Submitted: 2026-09-15

Updated: 2026-09-15

Code: https://github.com/cza1006/llm-persuasion-defense

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Trace — 9× in this paper · explained in 2 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Iterative Refinement — 3× in this paper · explained in 3 episodes
• LLM judge — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 4 episodes
• Entrainment — 2× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 10 episodes
• Hallucination — 1× in this paper · explained in 4 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Markov Decision Process — 1× in this paper
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Mechanistic interpretability — 1× in this paper · explained in 2 episodes
• Persuasion Success Rate — 1× in this paper

Sources

• Must Read: A Comprehensive Survey of Computational Persuasion
• Llama See, Llama Do: A Mechanistic Perspective on Contextual Entrainment and Distraction in LLMs
• Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack

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