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LLM agents' opinions shift based on campaign timing and natural drift

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper explores how large language model (LLM) agents' opinions change when subjected to competing influence campaigns. The study found that the timing of campaign spending and the LLMs' natural evolution significantly impact observed opinion shifts. After accounting for baseline drift, campaign-driven changes more closely aligned with a claim's factual basis than the campaigner's assigned role. The research also highlighted that LLM populations tend to lose stance diversity over time and that campaigns can reduce differences between segregated communities. AI

IMPACT This research provides insights into how LLM agents can be influenced and how their opinions evolve, which could inform the design of more robust AI systems and understanding of AI interactions.

RANK_REASON The cluster contains an academic paper detailing research on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM agents' opinions shift based on campaign timing and natural drift

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The cluster contains an academic paper detailing research on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Nicolas Fay ·

    Competing Influence in LLM Populations: Budget Timing and Baseline Drift

    When competing campaigns target a population of large language model (LLM) agents, observed opinion changes may reflect both campaign influence and changes that would occur without intervention. We study how the timing of campaign expenditure and the population's own evolution sh…

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