Summary of Large Language Models For Market Research: a Data-augmentation Approach, by Mengxin Wang (naveen Jindal School Of Management et al.
Large Language Models for Market Research: A Data-augmentation Approach
by Mengxin Wang, Dennis J. Zhang, Heng Zhang
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper proposes a novel approach to integrating Large Language Model (LLM)-generated data with real data in conjoint analysis. The authors highlight the limitations of traditional survey-based methods and the significant gap between LLM-generated and human data, which can introduce biases when substituting between them. They introduce a statistical data augmentation method that leverages transfer learning principles to debias the LLM-generated data using a small amount of human data. This approach results in statistically robust estimators with consistent and asymptotically normal properties. The authors validate their framework through empirical studies on COVID-19 vaccine preferences and sports car choices, demonstrating its ability to reduce estimation error and save data and costs. They also show that naive approaches fail to save data due to the inherent biases in LLM-generated data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper talks about how computers can help us understand what people like or dislike. Right now, we have two ways to do this: asking people questions directly or using computer programs to generate fake answers that might not be accurate. The problem is that these fake answers can actually make it harder for us to know what people really think. To fix this, the authors came up with a new way to combine real and fake answers in a way that helps us avoid mistakes. They tested their idea on two different topics: COVID-19 vaccines and sports cars. The results showed that their method is better at giving accurate answers than just using the fake answers or asking people questions directly. |
Keywords
» Artificial intelligence » Data augmentation » Large language model » Transfer learning
