# Neural network embeddings recover value dimensions from psychometric survey items on par with human data

arXiv: https://arxiv.org/abs/2509.24906 · HTML: https://mlsanity.com/paper/2509.24906/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [jina-embeddings-v3: Multilingual Embeddings With Task LoRA](https://mlsanity.com/paper/2409.10173/) (2024)
- [KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model](https://mlsanity.com/paper/2501.01028/) (2025)
- [No Training Required: Exploring Random Encoders for Sentence Classification](https://mlsanity.com/paper/1901.10444/) (2019)
- [Quantifying social organization and political polarization in online platforms](https://mlsanity.com/paper/2010.00590/) (2020)
- [What does ChatGPT return about human values? Exploring value bias in ChatGPT using a descriptive value theory](https://mlsanity.com/paper/2304.03612/) (2023)
- [Deep Lexical Hypothesis: Identifying personality structure in natural language](https://mlsanity.com/paper/2203.02092/) (2022)

## Built on by (most cited first, 2 of 2)

- [Quantifying and Mitigating Socially Desirable Responding in LLMs: A Desirability-Matched Graded Forced-Choice Psychometric Study](https://mlsanity.com/paper/2602.17262/) (2026)
- [Stories of Your Life as Others: A Round-Trip Evaluation of LLM-Generated Life Stories Conditioned on Rich Psychometric Profiles](https://mlsanity.com/paper/2604.06071/) (2026)

## Abstract (arXiv)

We demonstrate that embeddings derived from large language models, when processed with "Survey and Questionnaire Item Embeddings Differentials" (SQuID), can recover the structure of human values obtained from human rater judgments on the Revised Portrait Value Questionnaire (PVQ-RR). We compare multiple embedding models across a number of evaluation metrics including internal consistency, dimension correlations and multidimensional scaling configurations. Unlike previous approaches, SQuID addresses the challenge of obtaining negative correlations between dimensions without requiring domain-specific fine-tuning or training data re-annotation. Quantitative analysis reveals that our embedding-based approach explains 55% of variance in dimension-dimension similarities compared to human data. Multidimensional scaling configurations show alignment with pooled human data from 49 different countries. Generalizability tests across three personality inventories (IPIP, BFI-2, HEXACO) demonstrate that SQuID consistently increases correlation ranges, suggesting applicability beyond value theory. These results show that semantic embeddings can effectively replicate psychometric structures previously established through extensive human surveys. The approach offers substantial advantages in cost, scalability and flexibility while maintaining comparable quality to traditional methods. Our findings have significant implications for psychometrics and social science research, providing a complementary methodology that could expand the scope of human behavior and experience represented in measurement tools.