mlsanityData to 14 Oct 2026

Guided Persona-based AI Surveys: Can we replicate personal mobility preferences at scale using LLMs?

arXiv 2501.13955 ↗First posted 20 Jan 2025cs.CL cs.AI cs.CYcited by 3

On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment

Abstract (arXiv)

This study explores the potential of Large Language Models (LLMs) to generate artificial surveys, with a focus on personal mobility preferences in Germany. By leveraging LLMs for synthetic data creation, we aim to address the limitations of traditional survey methods, such as high costs, inefficiency and scalability challenges. A novel approach incorporating "Personas" - combinations of demographic and behavioural attributes - is introduced and compared to five other synthetic survey methods, which vary in their use of real-world data and methodological complexity. The MiD 2017 dataset, a comprehensive mobility survey in Germany, serves as a benchmark to assess the alignment of synthetic data with real-world patterns. The results demonstrate that LLMs can effectively capture complex dependencies between demographic attributes and preferences while offering flexibility to explore hypothetical scenarios. This approach presents valuable opportunities for transportation planning and social science research, enabling scalable, cost-efficient and privacy-preserving data generation.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 5 of its 10 references that are in the map. First posted on arXiv 20 Jan 2025.

Builds on

5 of 10 references.

References · 5

  1. 2024Large Language Models for Data Annotation and Synthesis: A Surveycited by 205
  2. 2024A Survey on Data Synthesis and Augmentation for Large Language Modelscited by 41
  3. 2024Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Studycited by 14
  4. 2024An evaluation framework for synthetic data generation modelscited by 5
  5. 2024Creating an LLM-based AI-agent: A high-level methodology towards enhancing LLMs with APIscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Aligning LLM agents with human learning and adjustment behavior: a dual agent approachcited by 3
  2. 2026The conditional superiority of fast silicon samplingcited by 1
  3. 2026Understanding Reliability in LLM-based Human Behavior Simulationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026From Survey Personas to LLM Agents: A Generative Agent-based Simulation of Mobility Policy Preference Dynamics
  2. 2024Be More Real: Travel Diary Generation Using LLM Agents and Individual Profiles
  3. 2024Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
  4. 2025Aligning LLM with human travel choices: a persona-based embedding learning approach
  5. 2025TrajLLM: A Modular LLM-Enhanced Agent-Based Framework for Realistic Human Trajectory Simulation
  6. 2025German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies
  7. 2024Using LLMs to Model the Beliefs and Preferences of Targeted Populations
  8. 2025Graph RAG as Human Choice Model: Building a Data-Driven Mobility Agent with Preference Chain