Summary of Are Large Language Models Chameleons? An Attempt to Simulate Social Surveys, by Mingmeng Geng et al.
Are Large Language Models Chameleons? An Attempt to Simulate Social Surveys
by Mingmeng Geng, Sihong He, Roberto Trotta
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 (LLMs) can simulate social surveys with varying degrees of bias and variability, according to a new study. The researchers conducted millions of simulations where LLMs answered subjective questions, comparing the results to European Social Survey data. They found that prompts have a significant effect on bias and variability, highlighting cultural, age, and gender disparities. To measure these differences, the team proposed a novel statistical method inspired by Jaccard similarity. Furthermore, they emphasized the importance of analyzing prompt robustness and variability before using LLMs for social survey simulation, as their imitation abilities are approximate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can pretend to answer social surveys, but they don’t always get it right. Researchers tested how well these AI models answered tricky questions by comparing them to real data from a big social survey. They found that the way you ask the question affects the answers and that some biases show up. The team came up with a new way to measure these differences using Jaccard similarity, which is like a special formula. It’s important to test how well AI models can answer questions before using them to pretend to do social surveys. |
Keywords
» Artificial intelligence » Prompt
