# Prompt fidelity of ChatGPT4o / Dall-E3 text-to-image visualisations

arXiv: https://arxiv.org/abs/2510.21821 · HTML: https://mlsanity.com/paper/2510.21821/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Generative Discrimination: What Happens When Generative AI Exhibits Bias, and What Can Be Done About It](https://mlsanity.com/paper/2407.10329/) (2024)

## Abstract (arXiv)

This study examines the prompt fidelity of ChatGPT4o / DALL-E3 text-to-image visualisations by analysing whether attributes explicitly specified in autogenously generated prompts are correctly rendered in the resulting images. Using two public-domain datasets comprising 200 visualisations of women working in the cultural and creative industries and 230 visualisations of museum curators, the study assessed accuracy across personal attributes (age, hair), appearance (attire, glasses), and paraphernalia (name tags, clipboards). While correctly rendered in most cases, DALL-E3 deviated from prompt specifications in 15.6% of all attributes (n=710). Errors were lowest for paraphernalia, moderate for personal appearance, and highest for depictions of the person themselves, particularly age. These findings demonstrate measurable prompt-to-image fidelity gaps with implications for bias detection and model evaluation.