# Large Language Models for Market Research: A Data-augmentation Approach

arXiv: https://arxiv.org/abs/2412.19363 · HTML: https://mlsanity.com/paper/2412.19363/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 26 Dec 2024.

## Placed in

- Synthetic data and self-improvement for LLMs / [LLM synthetic data generation](https://mlsanity.com/synth/llm-synthetic-data-generation/)

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities](https://mlsanity.com/paper/2408.13296/) (2024)
- [The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective](https://mlsanity.com/paper/2312.15524/) (2023)
- [Large Language Models: An Applied Econometric Framework](https://mlsanity.com/paper/2412.07031/) (2024)
- [Social Science Meets LLMs: How Reliable Are Large Language Models in Social Simulations?](https://mlsanity.com/paper/2410.23426/) (2024)
- [Are Large Language Models (LLMs) Good Social Predictors?](https://mlsanity.com/paper/2402.12620/) (2024)

## Built on by (most cited first, 20 of 20)

- [What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce](https://mlsanity.com/paper/2508.02630/) (2025)
- [Auto-Formulating Dynamic Programming Problems with Large Language Models](https://mlsanity.com/paper/2507.11737/) (2025)
- [Distribution Shift Alignment Helps LLMs Simulate Survey Response Distributions](https://mlsanity.com/paper/2510.21977/) (2025)
- [LLM-SAA: LLM-persona Generated Distributions for Decision-making](https://mlsanity.com/paper/2602.06357/) (2026)
- [Robustness is Important: Limitations of LLMs for Data Fitting](https://mlsanity.com/paper/2508.19563/) (2025)
- [Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management](https://mlsanity.com/paper/2510.03310/) (2025)
- [Generative Augmented Inference of LLM-generated Data for Market Research: Theory and Empirical Evidence](https://mlsanity.com/paper/2604.14575/) (2026)
- [Synthetic Personalities: How Well Can LLMs Mimic Individual Respondents Using Socio-Economic Microdata?](https://mlsanity.com/paper/2606.04592/) (2026)
- [Characterizing the ability of LLMs to recapitulate Americans' distributional responses to public opinion polling questions across political issues](https://mlsanity.com/paper/2603.20229/) (2026)
- [When Can LLM Digital Twins Reduce Human Measurement? From Behavioral Fidelity to Statistical Substitutability](https://mlsanity.com/paper/2609.07987/) (2026)
- [Improving Behavioral Alignment in LLM Social Simulations via Context Formation and Navigation](https://mlsanity.com/paper/2601.01546/) (2026)
- [LIBRA: Language Model Informed Bandit Recourse Algorithm for Personalized Treatment Planning](https://mlsanity.com/paper/2601.11905/) (2026)
- [AI-Generated Measurements for Identification and Inference with Missing Data: A Weak Shadow Variable Approach](https://mlsanity.com/paper/2602.16061/) (2026)
- [Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys](https://mlsanity.com/paper/2604.17267/) (2026)
- [LLM Biases](https://mlsanity.com/paper/2604.26960/) (2026)
- [LLM-Powered Virtual Population for Demand Simulation and Pricing](https://mlsanity.com/paper/2606.16183/) (2026)
- [SCAPE: Scenario-Conditioned Simulation-Augmented Policy Evaluation](https://mlsanity.com/paper/2608.19425/) (2026)
- [Learning a Size-Weight Frontier for Synthetic-Augmented Inference](https://mlsanity.com/paper/2608.28576/) (2026)
- [Distributional Validity of a Korean Synthetic Persona Panel: Evidence From the Korea Media Panel Survey](https://mlsanity.com/paper/2608.28615/) (2026)
- [PriceBench: A Diagnostic Benchmark for Price, Quality, and Brand Preferences in LLM Booking Agents](https://mlsanity.com/paper/2609.31468/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Their ability to generate human-like text has opened new possibilities for market research, particularly in conjoint analysis, where understanding consumer preferences is essential but often resource-intensive. Traditional survey-based methods face limitations in scalability and cost, making LLM-generated data a promising alternative. However, while LLMs have the potential to simulate real consumer behavior, recent studies highlight a significant gap between LLM-generated and human data, with biases introduced when substituting between the two. In this paper, we address this gap by proposing a novel statistical data augmentation approach that efficiently integrates LLM-generated data with real data in conjoint analysis. This results in statistically robust estimators with consistent and asymptotically normal properties, in contrast to naive approaches that simply substitute human data with LLM-generated data, which can exacerbate bias. We further present a finite-sample performance bound on the estimation error. We validate our framework through an empirical study on COVID-19 vaccine preferences, demonstrating its superior ability to reduce estimation error and save data and costs by 24.9% to 79.8%. In contrast, naive approaches fail to save data due to the inherent biases in LLM-generated data compared to human data. Another empirical study on sports car choices validates the robustness of our results. Our findings suggest that while LLM-generated data is not a direct substitute for human responses, it can serve as a valuable complement when used within a robust statistical framework.