Summary of Do Large Language Models Understand Verbal Indicators Of Romantic Attraction?, by Sandra C. Matz et al.
Do Large Language Models Understand Verbal Indicators of Romantic Attraction?
by Sandra C. Matz, Heinrich Peters, Paul W. Eastwick, Moran Cerf, Eli J. Finkel
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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) have been shown to detect romantic attraction during brief getting-to-know-you interactions, such as speed dating. The study examined data from 964 speed dates and found that ChatGPT (and Claude 3) can predict both objective and subjective indicators of speed dating success with a high degree of accuracy (r=0.12-0.23). ChatGPT’s predictions were not only on par with those of human judges but also incremental to the speed daters’ own predictions. The model’s judgments showed substantial overlap with those made by human observers (mean r=0.29), highlighting similarities in their representation of romantic attraction that is, partially, independent of accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers used a computer program called ChatGPT to study what makes people click on a first date and become attracted to each other. They looked at data from 964 speed dates and found that the computer program can predict how successful the dates are with a pretty good degree of accuracy. The program’s predictions were just as good as those made by humans who watched the same videos! This shows that computers can understand what makes people attracted to each other, even if it’s not always easy for us to figure out. |
Keywords
» Artificial intelligence » Claude
