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arXiv · 2609.14849

LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions

Myra Cheng·Lujain Ibrahim·Grace Liu·Michelle S. Lam·Vishakh Padmakumar·Nick Madibekov·Diyi Yang·Dan Jurafsky
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Abstract

We characterize how people are turning to LLMs as oracles: all-knowing authorities on subjective personal questions. Motivated by risks to users' autonomy and well-being, we develop a typology and LLM-based methods to measure this form of AI reliance at scale and understand how people are offloading judgment and decision-making to AI. Applying our typology to public usage data (68K prompts from WildChat and ThoughtTrace), we find that LLM-as-oracle use has increased over time (2023-2026) and is more prevalent among younger users. We further build a privacy-preserving data donation tool to analyze individuals' longitudinal usage data (140K prompts from 52 participants), identifying similar trends. People are often unaware of their own LLM-as-oracle use, and express dissatisfaction with this behavior after seeing our tool's analysis. Finally, we identify two drivers of LLM-as-oracle use: people's perceptions of AI and the behavior of AI models themselves, which motivate possible interventions to support users' self-deliberation.

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Myra Cheng, Lujain Ibrahim, Grace Liu, Michelle S. Lam, Vishakh Padmakumar, Nick Madibekov, Diyi Yang, Dan Jurafsky. 2026-09-13. LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions. https://arxiv.org/abs/2609.14849

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