# Exploring LLMs for Automated Generation and Adaptation of Questionnaires

arXiv: https://arxiv.org/abs/2501.05985 · HTML: https://mlsanity.com/paper/2501.05985/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [Persona Generators: Generating Diverse Synthetic Personas for Arbitrary Contexts](https://mlsanity.com/paper/2602.03545/) (2026)
- [Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation](https://mlsanity.com/paper/2510.13884/) (2025)
- [Equitable Evaluation via Elicitation](https://mlsanity.com/paper/2602.21327/) (2026)

## Abstract (arXiv)

Effective questionnaire design improves the validity of the results, but creating and adapting questionnaires across contexts is challenging due to resource constraints and limited expert access. Recently, the emergence of LLMs has led researchers to explore their potential in survey research. In this work, we focus on the suitability of LLMs in assisting the generation and adaptation of questionnaires. We introduce a novel pipeline that leverages LLMs to create new questionnaires, pretest with a target audience to determine potential issues and adapt existing standardized questionnaires for different contexts. We evaluated our pipeline for creation and adaptation through two studies on Prolific, involving 238 participants from the US and 118 participants from South Africa. Our findings show that participants found LLM-generated text clearer, LLM-pretested text more specific, and LLM-adapted questions slightly clearer and less biased than traditional ones. Our work opens new opportunities for LLM-driven questionnaire support in survey research.