Summary of The Generation Gap: Exploring Age Bias in the Value Systems Of Large Language Models, by Siyang Liu et al.
The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models
by Siyang Liu, Trish Maturi, Bowen Yi, Siqi Shen, Rada Mihalcea
First submitted to arxiv on: 12 Apr 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 The paper explores the alignment of values in Large Language Models (LLMs) with specific age groups, leveraging data from the World Value Survey across thirteen categories. The study finds a general inclination of LLM values towards younger demographics, especially when compared to the US population. However, this inclination can vary across different value categories. Additionally, the paper investigates the impact of incorporating age identity information in prompts and observes challenges in mitigating value discrepancies with different age cohorts. The findings highlight the age bias in LLMs and provide insights for future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand and generate human-like language. Researchers want to know if these models think like young or old people do. They used a big survey called World Value Survey, which asked questions about what’s important in life. The results show that the LLMs tend to agree with younger people more than older people. This is interesting because it could mean that the LLMs are not fair to everyone. The researchers also tried using information about age when asking the LLMs questions and found some problems. Overall, this study helps us understand how these super smart computers think. |
Keywords
» Artificial intelligence » Alignment
