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LLMs show promise in personalizing behavioral nudges with reduced data needs

By PulseAugur Editorial · [1 sources] ·

A new research paper explores how large language models (LLMs) can be used to design personalized "decoy nudges" that influence behavior. The study found that LLM-identified optimal decoy parameters improved nudging effects in the United States and Germany, though not in other tested countries. Furthermore, LLM-generated data can enhance Bayesian choice models, potentially reducing the required human sample size by up to 75% while maintaining predictive performance. This suggests LLMs can be cost-effective tools for developing personalized nudges, especially when combined with limited human data. AI

IMPACT LLMs can reduce costs and improve efficiency in behavioral science research and personalized marketing.

RANK_REASON Research paper published on arXiv detailing the use of LLMs for personalized nudges. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

LLMs show promise in personalizing behavioral nudges with reduced data needs

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Research paper published on arXiv detailing the use of LLMs for personalized nudges. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Vladimir Maksimenko, Leonard Lee, Prateek Bansal ·

    Integrating Large Language Models with Human Data to Design Personalized Decoy Nudges

    arXiv:2508.12045v3 Announce Type: replace-cross Abstract: Although nudge strategies can influence behavior, their effectiveness varies across individuals, highlighting the need for personalized nudge design. We hypothesize that large language models (LLMs) can facilitate the deve…

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