The paper addresses the problem of potential misuse of LLMs for large-scale disinformation campaigns due to their increasing persuasiveness and personalization capabilities.
It explores how LLMs can be used to effectively persuade individuals and the implications for online manipulation.
This paper proposes and tests different LLM-driven persuasion strategies in interactive debates to identify methods that maximize opinion change in humans.
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https://arxiv.org/abs/2501.17273
📌 The Mixed approach uses a simple multi-agent system for improved LLM persuasion. It leverages specialized agents for personalization and statistical fabrication. This modular design allows for targeted strategy, enhancing persuasiveness over monolithic models.
📌 Fabricated statistics, when combined with personalization, become surprisingly effective. The paper highlights that LLMs can generate convincing, albeit false, data. This raises concerns about automated disinformation, even with unsophisticated methods.
📌 The limited success of direct personalization suggests current LLMs struggle with nuanced user profiling. The multi-agent system's scratchpad likely aids in focusing personalization. This implies that effective LLM personalization needs improved context distillation.
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Methods Explored in this Paper 🔧:
→ The researchers designed a platform for human-LLM debate experiments.
→ They compared static human-written arguments, static LLM-generated arguments, and four types of LLM debates: Simple, Stats, Personalized, and Mixed.
→ The Simple debate used a basic prompt for the LLM to persuade.
→ The Stats debate instructed the LLM to use fabricated but realistic-sounding statistics.
→ The Personalized debate provided the LLM with user demographics and personality traits to tailor arguments.
→ The Mixed debate employed a multi-agent system with personalized and stats agents, plus an executive agent to synthesize responses. This approach combined personalized arguments with fabricated statistics.
→ Participants' opinions were measured using a seven-point Likert scale before and after each interaction to quantify opinion change.
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Key Insights 💡:
→ Static arguments from humans and basic LLMs have similar persuasive power.
→ The Mixed strategy, combining personalization and fabricated statistics in interactive debates, is significantly more persuasive than static human arguments.
→ Simply personalizing arguments by providing demographic and personality data to an LLM does not improve persuasiveness and can even reduce it compared to simpler approaches.
→ Using fabricated statistics alone shows comparable persuasiveness to basic LLM arguments.
→ The multi-agent 'Mixed' approach likely benefits from focused strategy and improved statistic targeting.
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Results 📊:
→ The Mixed strategy had a 51% chance of persuading participants to change their initial opinion.
→ Static human-written arguments had only a 32% chance of persuasion.
→ The Personalized debate type showed a lower probability of opinion change at 34% compared to the Simple debate type at 42.7%.
→ Likert Delta, measuring the magnitude of opinion shift, was highest for the Mixed strategy at 1.146, compared to 0.833 for static human arguments and 0.782 for the Simple debate.
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