Summary of Large Language Models As Mirrors Of Societal Moral Standards, by Evi Papadopoulou et al.
Large Language Models as Mirrors of Societal Moral Standards
by Evi Papadopoulou, Hadi Mohammadi, Ayoub Bagheri
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Symbolic Computation (cs.SC)
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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 investigates the capabilities of language models to represent moral norms across various cultural contexts. Building upon prior research, this study aims to replicate and expand these findings by examining issues like homosexuality and divorce using data from two surveys: the World Values Survey (WVS) and the Pew Research Center’s (PEW). The authors evaluate the performance of monolingual and multilingual models against these moral perspectives from over 40 countries. The results indicate that biases exist in all models, with limited success in capturing cultural nuances. Notably, the BLOOM model demonstrates better performance, exhibiting some positive correlations, although it still falls short of achieving a comprehensive understanding of diverse cultures’ moral intricacies. This research underscores the limitations of current Pre-Trained Language Models (PLMs) in processing cross-cultural differences and emphasizes the importance of developing culturally aware AI systems aligned with universal human values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well computer models can understand different cultural norms about things like same-sex relationships and divorce. The researchers used data from two big surveys that asked people all over the world about their moral beliefs. They found that these computer models are not very good at understanding the differences between cultures. Even the best model, called BLOOM, didn’t do a great job of getting it right. This shows us that we need to make better AI systems that can understand different cultural perspectives and values. |
