Summary of Measuring Human and Ai Values Based on Generative Psychometrics with Large Language Models, by Haoran Ye et al.
Measuring Human and AI Values Based on Generative Psychometrics with Large Language Models
by Haoran Ye, Yuhang Xie, Yuanyi Ren, Hanjun Fang, Xin Zhang, Guojie Song
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces Generative Psychometrics for Values (GPV), an AI-based framework for measuring human values in text-revealed selective perceptions. GPV is theoretically grounded in traditional psychometrics, but dynamically parses unstructured texts into perceptions akin to static stimuli. The authors demonstrate the stability, validity, and superiority of GPV over prior psychological tools using human-authored blogs. They also extend GPV to measure AI model values, enabling context-specific measurement and bridging LLM values with their safety. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence (AI) to understand how people think about important things like morality and ethics. The researchers created a new way to measure these ideas by looking at what people write online. They tested this method on human-written blogs and found it was more accurate than other methods. Then, they used the same approach to see if AI models, like language models, have their own values too. This could help us make sure AI is safe and works well with humans. |
