# Stories of Your Life as Others: A Round-Trip Evaluation of LLM-Generated Life Stories Conditioned on Rich Psychometric Profiles

arXiv: https://arxiv.org/abs/2604.06071 · HTML: https://mlsanity.com/paper/2604.06071/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas](https://mlsanity.com/paper/2408.06929/) (2024)
- [The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs](https://mlsanity.com/paper/2509.03730/) (2025)
- [LLMs Simulate Big Five Personality Traits: Further Evidence](https://mlsanity.com/paper/2402.01765/) (2024)
- [The Need for a Socially-Grounded Persona Framework for User Simulation](https://mlsanity.com/paper/2601.07110/) (2026)
- [IMPersona: Evaluating Individual Level LM Impersonation](https://mlsanity.com/paper/2504.04332/) (2025)
- [Probing then Editing Response Personality of Large Language Models](https://mlsanity.com/paper/2504.10227/) (2025)
- [Deep Binding of Language Model Virtual Personas: a Study on Approximating Political Partisan Misperceptions](https://mlsanity.com/paper/2504.11673/) (2025)
- [PersonaEval: Are LLM Evaluators Human Enough to Judge Role-Play?](https://mlsanity.com/paper/2508.10014/) (2025)
- [Personality Expression Across Contexts: Linguistic and Behavioral Variation in LLM Agents](https://mlsanity.com/paper/2602.01063/) (2026)
- [Scaling Law in LLM Simulated Personality: More Detailed and Realistic Persona Profile Is All You Need](https://mlsanity.com/paper/2510.11734/) (2025)
- [Neural network embeddings recover value dimensions from psychometric survey items on par with human data](https://mlsanity.com/paper/2509.24906/) (2025)
- [Can LLMs Truly Embody Human Personality? Analyzing AI and Human Behavior Alignment in Dispute Resolution](https://mlsanity.com/paper/2602.07414/) (2026)

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

- [Misalignment Has a Personality: A Big Five Account of Emergent Misalignment](https://mlsanity.com/paper/2607.26389/) (2026)
- [The Communication Bottleneck: A Round-Trip Study of Tree-Structured Expression Serialization in Language Models](https://mlsanity.com/paper/2609.21509/) (2026)

## Abstract (arXiv)

Personality traits are richly encoded in natural language, and large language models (LLMs) trained on human text can simulate personality when conditioned on persona descriptions. However, existing evaluations rely predominantly on questionnaire self-report by the conditioned model, are limited in architectural diversity, and rarely use real human psychometric data. Without addressing these limitations, it remains unclear whether personality conditioning produces psychometrically informative representations of individual differences or merely superficial alignment with trait descriptors. To test how robustly LLMs can encode personality into extended text, we condition LLMs on real psychometric profiles from 290 participants to generate first-person life story narratives, and then task independent LLMs to recover personality scores from those narratives alone. We show that personality scores can be recovered from the generated narratives at levels approaching human test-retest reliability (mean r = 0.750, 85% of the human ceiling), and that recovery is robust across 10 LLM narrative generators and 3 LLM personality scorers spanning 6 providers. Decomposing systematic biases reveals that scoring models achieve their accuracy while counteracting alignment-induced defaults. Content analysis of the generated narratives shows that personality conditioning produces behaviourally differentiated text: nine of ten coded features correlate significantly with the same features in participants' real conversations, and personality-driven emotional reactivity patterns in narratives replicate in real conversational data. These findings provide evidence that the personality-language relationship captured during pretraining supports robust encoding and decoding of individual differences, including characteristic emotional variability patterns that replicate in real human behaviour.