💬 NLP

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

This paper analyzes the value trade-off structures in everyday advice-seeking contexts using a curated dataset from four subreddits and finds that large language models consistently prioritize "Exploration & Growth" over "Benevolence & Connection," raising concerns about potential value homogenization in AI-mediated decision-making.

Original authors: Zhiyi Chen, Eun Cheol Choi, Yingjia Luo, Xinyi Wang, Yulei Xiao, Aizi Yang, Luca Luceri

Published 2026-02-05

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Zhiyi Chen, Eun Cheol Choi, Yingjia Luo, Xinyi Wang, Yulei Xiao, Aizi Yang, Luca Luceri

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are standing at a crossroads in your life. You have to choose between two paths: one leads to a safe, quiet town where you know everyone, and the other leads to a bustling city full of new opportunities but also uncertainty. This is a dilemma. In real life, these choices aren't about finding the "right" answer; they are about balancing different things we care about, like safety versus growth, or loyalty versus freedom.

This paper asks a big question: When people ask Large Language Models (LLMs) like ChatGPT for advice on these tough life choices, what kind of "personality" do these AI bots show? Do they act like cautious guardians, or do they act like adventurous explorers?

Here is a simple breakdown of what the researchers did and found:

1. The Map of Human Values (The "Menu")

First, the researchers needed a way to understand what people actually care about when they ask for advice. They didn't just guess; they looked at over 5,700 real stories from people asking for help on Reddit (in communities for career advice, friendship, men's advice, and women's advice).

The Analogy: Imagine they took a giant bag of mixed-up Lego bricks (the specific reasons people gave for their choices) and sorted them into boxes.
The Result: They built a four-level "Value Map."
- At the bottom, there were thousands of tiny, specific reasons (like "I want to stay close to my mom" or "I want to get a promotion").
- They grouped these up into bigger boxes until they found just four main categories that cover almost everything:
  1. Exploration & Growth: (Adventure, learning, becoming your best self).
  2. Security & Stability: (Safety, routine, protecting what you have).
  3. Achievement & Impact: (Success, winning, making a mark).
  4. Benevolence & Connection: (Caring for others, friendship, loyalty).

2. How Humans Clash (The "Tug-of-War")

The researchers looked at how these values fight against each other in real life.

The "Relationship" Communities: In advice forums for men, women, and friends, the biggest tug-of-war is usually Safety vs. Connection. People are often torn between "Is this safe?" and "Will this hurt my relationships?"
The "Career" Community: Here, the fight is different. It's mostly Safety vs. Growth. People are asking, "Should I keep my boring but safe job, or take a risky chance to grow my career?"
The "Women's" Community: This group had the most complex web of conflicts. It was like a crowded room where everyone was arguing about everything at once. Women often have to balance many competing demands (work, family, safety, self) all at the same time.

3. The AI's "Personality" (The "Coach")

Then, the researchers fed these real-life dilemmas to three different AI models (GPT-4o, DeepSeek, and Gemini) and asked them to pick a side. They wanted to see which "value" the AI preferred.

The Big Discovery:
Across the board, the AI models acted like enthusiastic life coaches who love change.

The Winner: Exploration & Growth. The AI consistently chose the path of "go for it," "try something new," and "develop yourself."
The Loser: Benevolence & Connection. The AI was the least likely to choose the option that prioritized caring for others, maintaining relationships, or sticking to social obligations.

The Metaphor:
Imagine you are asking a robot for advice on whether to move to a new city to chase a dream (Growth) or stay home to care for a sick relative (Connection).

The AI's instinct: "Go! Chase your dreams! The world is waiting!"
The Human instinct (often): "Stay. Family comes first."

The paper found that the AI is systematically biased toward self-development over relational care. It doesn't matter if the question is about a job, a friend, or a relationship; the AI tends to say, "Prioritize your own growth."

4. Why This Matters (The "Echo Chamber")

The researchers warn that this isn't just a quirk; it's a risk.

The Analogy: If you ask a thousand different people for advice, you get a thousand different perspectives. But if you ask a thousand different AI models, they all give you the same advice: "Grow, explore, and prioritize yourself."
The Consequence: This could lead to Value Homogenization. Imagine a world where AI advice slowly changes how we make decisions. If everyone's AI coach tells them to prioritize "Growth" over "Connection," we might start seeing a society where people are more independent but less connected, and where caring for others is seen as a secondary priority.

Summary

What they did: They mapped out how humans argue about values in real life and then tested if AI agrees with us.
What they found: Humans have complex, messy value conflicts that change depending on the situation (career vs. family).
The Twist: AI models are surprisingly consistent. They almost always pick Growth over Connection.
The Takeaway: AI isn't a neutral mirror of humanity; it has a specific "personality" that favors self-improvement and adventure, potentially nudging us away from the values of care and community.

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

Problem Statement

As Large Language Models (LLMs) become increasingly fluent and socially intuitive, users are relying on them not just for information retrieval but for advice in personal, social, and professional domains. However, advice-seeking rarely involves identifying a single correct answer; instead, it requires navigating complex trade-offs among competing values. Existing research on LLM value preferences suffers from three primary limitations:

Synthetic Benchmarks: Many evaluations rely on GPT-generated scenarios built from predefined templates, limiting scalability and the diversity of dilemmas.
Ecological Validity: Synthetic benchmarks often fail to capture how value trade-offs manifest in real-world settings.
Top-Down Taxonomies: Analyses typically rely on predefined value frameworks, which may leave many real-world cases unrepresented or underspecified.

This study addresses these gaps by investigating how LLMs navigate value trade-offs across different advice-seeking contexts using real-world data, aiming to characterize the underlying value structures and identify potential risks of value homogenization in AI-mediated advice.

Methodology

Data Collection

The authors leveraged a curated dataset of 5,728 real-world dilemmas sourced from four advice-oriented subreddits: r/AskMenAdvice, r/AskWomenAdvice, r/CareerAdvice, and r/FriendshipAdvice. Each post includes a textual description of a dilemma, two alternative options, and the associated costs and benefits.

Value Extraction and Validation

Using GPT-4o, the study extracted the core value motivating each option within the dilemmas.

Process: The model was prompted to summarize the core value (1–4 words) and provide a justification for each option.
Validation: A stratified random sample of 400 posts (800 value assignments) was manually reviewed by two authors. Inter-annotator agreement was high (Cohen's $\kappa = 0.833$ ), with 92% of assignments passing validation, confirming GPT-4o's reliability in this context.
Output: This process yielded 2,288 unique fine-grained values.

Bottom-Up Hierarchical Value Framework

Instead of using a top-down taxonomy, the authors inductively constructed a four-level hierarchical value framework:

Level 0: 2,288 fine-grained values.
Level 1: 175 clustered values (via k-means clustering on embeddings).
Level 2: 33 intermediate values.
Level 3: 4 top-level value dimensions.
- Achievement & Impact
- Benevolence & Connection
- Security & Stability
- Exploration & Growth

The framework's validity was confirmed through manual validation (92.4% agreement between annotators mapping bottom-level values to top-level categories) and showed substantial conceptual alignment with Schwartz's theory of basic human values.

LLM Evaluation

Three state-of-the-art LLMs (GPT-4o, DeepSeek-V3.2-Exp, and Gemini-2.5-Flash) were prompted to choose one option for each of the 5,728 dilemmas.

Controls: Temperature was set to 0 for stability. Order effects were mitigated by reversing option orders in a subset of experiments (92.5% of choices remained unchanged).
Metric: A winning rate was calculated for each top-level value. This metric represents the proportion of times a model preferred a specific value over another when both co-occurred in the same dilemma.
Analysis: Significance was assessed using non-parametric bootstrap tests to compare preferences across models and contexts.

Key Contributions

Methodological Pipeline: A scalable pipeline for assessing LLM value preferences using real-world, organically occurring dilemmas rather than synthetic benchmarks.
Data-Driven Value Framework: The inductive construction of a four-level hierarchical value framework from social media data, offering a data-driven alternative to top-down taxonomies.
Empirical Findings: Systematic evaluation of LLMs on real-world dilemmas revealing consistent value biases.
Dataset Release: Public release of the annotated dataset containing 5,728 dilemmas, extracted values, and LLM choices to support reproducible analysis.

Results

RQ1: Value Trade-off Structures in Subreddits

Heterogeneity: Value trade-off structures vary significantly across contexts. The women-focused subreddit (r/AskWomenAdvice) exhibited the highest network density, indicating more complex and diverse value conflicts compared to other subreddits.
Common Patterns: Men's, women's, and friendship-related subreddits shared highly correlated conflict patterns centered on Security tensions (e.g., Security vs. Respect, Security vs. Connection).
Distinct Structures: Career advice formed a distinct structure where Security frequently clashed with Self-Actualization and Growth.

RQ2: LLM Value Preferences

Across all three models and all contexts, a consistent and systematic bias was observed:

Exploration & Growth vs. Benevolence & Connection: LLMs consistently prioritized Exploration & Growth over Benevolence & Connection.
- Benevolence & Connection was the least preferred value across all models (significantly lower winning rates, $p < 0.001$ ).
- Exploration & Growth was consistently preferred over Security & Stability and Benevolence & Connection.
Contextual Nuance: While the overall preference for Growth over Care was robust, context-specific shifts were noted. Models placed higher weight on Security & Stability in women-focused advice compared to career advice, where Exploration & Growth became more prominent.
Model Consistency: The preference for Growth over Benevolence held true across GPT-4o, DeepSeek-V3.2-Exp, and Gemini-2.5-Flash, suggesting this is a systemic orientation rather than a model-specific artifact.

Significance and Claims

The paper claims that these findings highlight a potential risk of value homogenization in AI-mediated advice. By consistently prioritizing values related to self-development, autonomy, and growth over relational care, connection, and benevolence, LLMs may subtly shape decision-making and normative outcomes at scale.

The authors argue that as users increasingly rely on LLMs as substitutes for interpersonal counsel, these embedded value orientations could influence how individuals navigate trade-offs in their personal lives. The study does not claim that LLMs are "wrong," but rather that they are not value-neutral; they reflect and potentially amplify specific normative frameworks (favoring growth) while marginalizing others (relational care). This underscores the need for robust evaluation pipelines and governance strategies to ensure AI systems do not inadvertently reinforce dominant value systems at the expense of diverse human perspectives.

The study concludes modestly, acknowledging limitations such as the simplification of options to single dominant values and the reliance on a limited set of models. It calls for future work to examine downstream implications, user outcomes, and the need for diverse human judgments in the design of advice-giving AI systems.