Summary of Random Silicon Sampling: Simulating Human Sub-population Opinion Using a Large Language Model Based on Group-level Demographic Information, by Seungjong Sun et al.
Random Silicon Sampling: Simulating Human Sub-Population Opinion Using a Large Language Model Based on Group-Level Demographic Information
by Seungjong Sun, Eungu Lee, Dongyan Nan, Xiangying Zhao, Wonbyung Lee, Bernard J. Jansen, Jang Hyun Kim
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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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 Large language models have been shown to exhibit societal biases related to demographics, including race, gender, and others. Building on this idea, we propose a method called “random silicon sampling” that enables generating opinions aligning with those of humans by endowing language models with personalities based on demographic data. Our study analyzed two aspects: (1) a language model that generates survey responses matching a human group solely based on its demographic distribution and (2) the applicability of our methodology across various demographic subgroups and thematic questions. We found that language models can generate response distributions remarkably similar to actual U.S. public opinion polls using only group-level demographic information, as well as varying replicability depending on demographic groups and topic, due to inherent societal biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large computer programs called language models have a problem – they often reflect the biases of the people who created them. For example, if the programmers are mostly men, the model might be more likely to think that men are good at math. This paper proposes a way to make these language models better by giving them “personalities” based on demographic information like age and gender. The researchers tested this idea by analyzing how well the computer programs could match real-life public opinion polls in the United States. They found that the computer programs were surprisingly good at matching the opinions of different groups, but only if they used the right kind of demographic information. |
Keywords
» Artificial intelligence » Language model
