Growth First, Care Second? Tracing the Landscape of LLM Value Preferences in Everyday Dilemmas

TL;DR

This study analyzes how large language models (LLMs) navigate complex value trade-offs in advice-seeking contexts, revealing a consistent bias towards growth-related values and highlighting implications for AI-mediated decision-making.

Contribution

It introduces a hierarchical value framework for advice dilemmas and systematically compares LLM preferences with human value trade-offs across diverse social contexts.

Findings

LLMs prioritize exploration and growth over benevolence and connection.

Value trade-off structures vary significantly across different advice contexts.

LLMs show a systemic bias towards growth-related values, risking homogenization.

Abstract

People increasingly seek advice online from both human peers and large language model (LLM)-based chatbots. Such advice rarely involves identifying a single correct answer; instead, it typically requires navigating trade-offs among competing values. We aim to characterize how LLMs navigate value trade-offs across different advice-seeking contexts. First, we examine the value trade-off structure underlying advice seeking using a curated dataset from four advice-oriented subreddits. Using a bottom-up approach, we inductively construct a hierarchical value framework by aggregating fine-grained values extracted from individual advice options into higher-level value categories. We construct value co-occurrence networks to characterize how values co-occur within dilemmas and find substantial heterogeneity in value trade-off structures across advice-seeking contexts: a women-focused subreddit exhibits the highest network density, indicating more complex value conflicts; women's, men's, and friendship-related subreddits exhibit highly correlated value-conflict patterns centered on security-related tensions (security vs. respect/connection/commitment); by contrast, career advice forms a distinct structure where security frequently clashes with self-actualization and growth. We then evaluate LLM value preferences against these dilemmas and find that, across models and contexts, LLMs consistently prioritize values related to Exploration & Growth over Benevolence & Connection. This systemically skewed value orientation highlights a potential risk of value homogenization in AI-mediated advice, raising concerns about how such systems may shape decision-making and normative outcomes at scale.