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

TL;DR

The paper tackles the problem of understanding how LLMs navigate value trade-offs in everyday advice contexts. It develops a bottom-up, four-level hierarchical value framework derived from 5,728 real-world dilemmas on Reddit, extracting 2,288 distinct values and organizing them into four top-level dimensions aligned with Schwartz's theory. It then evaluates LLMs (GPT-4o, DeepSeek-V3.2-Exp, Gemini-2.5-Flash) on dilemma choices, revealing a robust pattern: Exploration & Growth is generally preferred, while Benevolence & Connection is least preferred, with context-specific shifts such as higher Security & Stability in women-focused contexts and more Growth emphasis in career contexts. The findings highlight potential risks of value homogenization in AI-mediated advice and offer methodological tools, data, and a framework for auditing LLM value preferences with practical implications for design, governance, and accountability in AI systems delivering everyday guidance.

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.

Figures (10)

• Figure 1: Example of a real-world dilemma sourced from r/AskWomenAdvice and the corresponding extracted values. Users describe a dilemma involving two mutually exclusive options (moving to a city vs. staying in a small town), each associated with different perceived benefits and costs. Our pipeline identifies the dominant core value motivating each option (Fulfillment vs. Stability), illustrating how everyday dilemmas encode value trade-offs.
• Figure 2: The hierarchical value framework constructed in this study through a bottom-up approach. The framework consists of four levels, with the top level comprising four higher-order value dimensions: Achievement & Impact, Benevolence & Connection, Security & Stability, and Exploration & Growth. Starting from fine-grained values extracted from real-world dilemmas (Level$_0$), semantically similar values are clustered into increasingly abstract value concepts at intermediate layers (Level$_1$ and Level$_2$), ultimately converging into four top-level value dimensions (Level$_3$).
• Figure 3: Value co-occurrence networks at Level$_2$ across four advice-oriented subreddits: r/AskMenAdvice, r/AskWomenAdvice, r/CareerAdvice, and r/FriendshipAdvice. Nodes represent values and edges indicate co-occurrence frequency within the same dilemma, with thicker edges reflecting stronger co-occurrence. Node colors correspond to the top-level values derived from our hierarchical framework: Security & Stability (orange), Benevolence & Connection (yellow), Exploration & Growth (blue), and Achievement & Impact (light blue).
• Figure 4: Illustrative examples of value trade-offs from r/CareerAdvice and r/FriendshipAdvice. In both dilemmas, users face two mutually exclusive options that involve competing values (e.g., Stability vs. Growth in r/CareerAdvice, Security vs. Commitment in r/FriendshipAdvice).
• Figure 5: Value preferences across three large language models (GPT-4o, DeepSeek-V3.2-Exp, and Gemini-2.5-Flash). Scores reflect the winning rates of each value. Error bars indicate 95% bootstrap confidence intervals ($B=1{,}000$). Across models, Benevolence & Connection receives the lowest winning rate, while Exploration & Growth consistently receives the highest winning rate.