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

• URL: http://arxiv.org/abs/2412.19363v2
• Date: Mon, 06 Jan 2025 17:33:20 GMT
• Title: Large Language Models for Market Research: A Data-augmentation Approach
• Authors: Mengxin Wang, Dennis J. Zhang, Heng Zhang,
• Abstract summary: Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Recent studies highlight a significant gap between LLM-generated and human data, with biases introduced when substituting between the two. We propose a novel statistical data augmentation approach that efficiently integrates LLM-generated data with real data in conjoint analysis.
• Score: 3.3199591445531453
• License:
• Abstract: 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. Our method leverages transfer learning principles to debias the LLM-generated data using a small amount of human data. 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 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.

Related papers

• Few-shot LLM Synthetic Data with Distribution Matching [37.55363714371521]
  Large language models (LLMs) produce high-quality synthetic data to enhance the performance of smaller models. LLMs-generated synthetic data often differs from the real data in key language attributes. We introduce SynAlign: a synthetic data generation and filtering framework based on key attribute distribution matching.
  arXiv  Detail & Related papers  (2025-02-09T16:43:32Z)
• Guided Persona-based AI Surveys: Can we replicate personal mobility preferences at scale using LLMs? [1.7819574476785418]
  This study explores the potential of Large Language Models (LLMs) to generate artificial surveys. By leveraging LLMs for synthetic data creation, we aim to address the limitations of traditional survey methods. A novel approach incorporating "Personas" is introduced and compared to five other synthetic survey methods.
  arXiv  Detail & Related papers  (2025-01-20T15:11:03Z)
• A text-to-tabular approach to generate synthetic patient data using LLMs [0.3628457733531155]
  We propose an approach to generate synthetic patient data that does not require access to the original data. We leverage prior medical knowledge and in-context learning capabilities of large language models to generate realistic patient data.
  arXiv  Detail & Related papers  (2024-12-06T16:10:40Z)
• Evaluating Language Models as Synthetic Data Generators [74.80905172696366]
  AgoraBench is a benchmark that provides standardized settings and metrics to evaluate LMs' data generation abilities. Through synthesizing 1.26 million training instances using 6 LMs and training 99 student models, we uncover key insights about LMs' data generation capabilities.
  arXiv  Detail & Related papers  (2024-12-04T19:20:32Z)
• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• Agentic Society: Merging skeleton from real world and texture from Large Language Model [4.740886789811429]
  This paper explores a novel framework that leverages census data and large language models to generate virtual populations. We show that our method produces personas with variability essential for simulating diverse human behaviors in social science experiments. But the evaluation result shows that only weak sign of statistical truthfulness can be produced due to limited capability of current LLMs.
  arXiv  Detail & Related papers  (2024-09-02T08:28:19Z)
• Entropy Law: The Story Behind Data Compression and LLM Performance [115.70395740286422]
  We find that model performance is negatively correlated to the compression ratio of training data, which usually yields a lower training loss. Based on the findings of the entropy law, we propose a quite efficient and universal data selection method. We also present an interesting application of entropy law that can detect potential performance risks at the beginning of model training.
  arXiv  Detail & Related papers  (2024-07-09T08:14:29Z)
• ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs [65.9625653425636]
  Large Language models (LLMs) exhibit harmful social biases. This work introduces a novel approach utilizing ChatGPT to generate synthetic training data.
  arXiv  Detail & Related papers  (2024-02-19T01:28:48Z)
• How to Train Data-Efficient LLMs [56.41105687693619]
  We study data-efficient approaches for pre-training language models (LLMs) We find that Ask-LLM and Density sampling are the best methods in their respective categories. In our comparison of 19 samplers, involving hundreds of evaluation tasks and pre-training runs, we find that Ask-LLM and Density are the best methods in their respective categories.
  arXiv  Detail & Related papers  (2024-02-15T02:27:57Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.