Value Portrait: Assessing Language Models’ Values through Psychometrically and Ecologically Valid Items

ACL 2025

Jongwook Han, Dongmin Choi, Woojung Song, Eun-Ju Lee, Yohan Jo

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🧭 Introduction

Existing value benchmarks rely on human or machine annotations that are vulnerable to value-related biases, and they are often evaluated in contexts that diverge from real-world contexts where LLMs generate text and express values. To address these issues, we introduce Value Portrait, a reliable benchmark for evaluating LLMs’ value orientations with two key characteristics:

• Real-world items: The benchmark consists of items that reflect real-world user–LLM interactions, improving ecological validity and real-world relevance.
• Psychometric validation: Human subjects rate each item by how similar it is to their own thoughts, and we compute correlations between these ratings and the subjects’ actual value scores. Items strongly correlated with specific values serve as reliable indicators for assessing those values.

Using Value Portrait, we evaluate 44 LLMs and find that these models tend to prioritize Benevolence, Security, and Self-Direction, while placing less emphasis on Tradition, Power, and Achievement. Our analysis further reveals biases in how LLMs perceive various demographic groups, deviating from real human data.

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📦 Installation

# Clone this repository
git clone https://github.com/holi-lab/ValuePortrait.git
cd ValuePortrait
# (Optional) Create a new environment
conda create -n vp python=3.10
conda activate vp
# Install dependencies
pip install -r requirements.txt

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🗂️ Data

The data/ directory contains the ValuePortrait dataset. The dataset consists of query-response pairs with tagged with the 10 Schwartz values and the 5 personality traits from the Big Five Inventory (BFI-10).

Tagged Data(data/query-response-tagged/)

Dataset	Description	Format
query-response-tagged.json	Query-response pairs where each response is tagged with correlation scores to the 10 Schwartz values and 5 Big Five personality traits	JSON

Query-Response Data (data/query-response/)

Dataset	Description	Format
DearAbby.json	Query-response pairs sourced from the Dear Abby advice column	JSON
LMSYS.json	Query-response pairs sourced from the LMSYS Chatbot Arena	JSON
Reddit.json	Query-response pairs sourced from the AITA subreddit	JSON
ShareGPT.json	Query-response pairs sourced from the ShareGPT dataset	JSON

Query Categories (data/query/)

Dataset	Description	Format
DearAbby.json	Original queries sourced from the Dear Abby advice column	JSON
LMSYS.json	Original queries sourced from the LMSYS Chatbot Arena	JSON
Reddit.json	Original queries sourced from the AITA subreddit	JSON
ShareGPT.json	Original queries sourced from the ShareGPT dataset	JSON
query_categorized.json	Categorized queries by topic for systematic analysis	JSON

Correlation Analysis Results (data/correlation_results/)

Dataset	Description	Format
pvq_correlation_results.json	Correlation analysis results on the 10 Schwartz values	JSON
higher_pvq_correlation_results.json	Higher-order Schwartz values correlation analysis results	JSON
bfi_correlation_results.json	Correlation analysis results on the 5 Big Five personality traits	JSON

Survey Data (data/prolific/)

Dataset	Description	Format
survey/main/survey.json	Main survey responses from Prolific participants	JSON
survey/pilot/	Pilot survey data for preliminary analysis	JSON
value/	The value orientations and personality traits of the Prolific participants measured using the PVQ-21 and BFI-10 questionnaires	JSON

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🧪 Experiments & LLM Evaluation Pipeline

This section explains how to reproduce our experiments and compute model value scores using the lm_evaluation pipeline.

• Step 1 — Generate raw model outputs

  • Script: lm_evaluation/main.py
  • Inputs: config/full_config.yaml, data/Phase1_total.json, prompts/
  • Outputs: JSON files under outputs/ (per provider/model/prompt/version)
  • Run:

    cd ValuePortrait
    # Option A: run all experiments from config
    python lm_evaluation/main.py
    # Option B: run a specific experiment by name
    python lm_evaluation/main.py <EXPERIMENT_NAME>

• Step 2 — Average responses across versions

  • Script: lm_evaluation/response_average.py
  • Reads: outputs/final/
  • Writes: average_outputs/
    • Per‑model files: {model_name}_averaged_results.json, {model_name}_metadata.json
  • Run:

    cd ValuePortrait
    python lm_evaluation/response_average.py

• Step 3 — Compute value dimension scores

  • Script: lm_evaluation/score.py
  • Reads: average_outputs/
  • Writes: score_results/final_results_0.3_pos_centered/ (per‑model score JSONs)
  • Logs: logs/score/
  • Run:

    cd ValuePortrait
    python lm_evaluation/score.py

All raw outputs, averaged results, and scoring results for the 44 evaluated models are stored under outputs/, average_outputs/, and score_results/, respectively.

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🧾 Citation

If you use this work, please cite:

@article{han2025value,
title={Value Portrait: Assessing Language Models' Values through Psychometrically and Ecologically Valid Items},
author={Han, Jongwook and Choi, Dongmin and Song, Woojung and Lee, Eun-Ju and Jo, Yohan},
journal={arXiv preprint arXiv:2505.01015},
year={2025}
}

🔑 License

This repository is released under the MIT License. See LICENSE for details.

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