Summary of Exploring Large Language Models on Cross-cultural Values in Connection with Training Methodology, by Minsang Kim and Seungjun Baek
Exploring Large Language Models on Cross-Cultural Values in Connection with Training Methodology
by Minsang Kim, Seungjun Baek
First submitted to arxiv on: 12 Dec 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 how large language models (LLMs) make judgments on cultural values across countries, examining the relationship between training methodologies like model sizes, training corpus, and alignment. Analysis shows that LLMs can judge socio-cultural norms similarly to humans but struggle with social systems and progress. Additionally, LLMs tend to be biased towards Western culture, which improves with multilingual training. Larger models show better understanding of social values, while smaller ones benefit from synthetic data. The study provides valuable insights into designing LLMs that understand cultural values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models learn about different cultures around the world. It compares how these models make decisions to how humans do it. The research finds that these models are good at understanding some cultural norms, but struggle with bigger issues like social systems and progress. Additionally, they often have a bias towards Western culture, which can be fixed by training them on many languages. The study also shows that bigger models are better at understanding social values, while smaller ones do well when given fake data to work with. Overall, the research gives us new ideas for how to design language models that really understand different cultures. |
Keywords
» Artificial intelligence » Alignment » Synthetic data
