Summary of Culturellm: Incorporating Cultural Differences Into Large Language Models, by Cheng Li et al.
CultureLLM: Incorporating Cultural Differences into Large Language Models
by Cheng Li, Mengzhou Chen, Jindong Wang, Sunayana Sitaram, Xing Xie
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposes CultureLLM, a cost-effective solution to incorporate cultural differences into large language models (LLMs). The existing methods for multilingual cultural data rely on prompt engineering or culture-specific pre-training, which might overlook the knowledge deficiency of low-resource cultures and require extensive computing resources. CultureLLM adopts World Value Survey (WVS) as seed data and generates semantically equivalent training data via proposed semantic data augmentation. The paper demonstrates that CultureLLM significantly outperforms various counterparts such as GPT-3.5 and Gemini Pro with comparable performance to GPT-4 or even better on 60 culture-related datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make computers understand different cultures better. Right now, computers are trained mainly with English data, which makes them biased towards Western cultures. To fix this, the authors propose a new way to train computers using data from a global survey called World Value Survey (WVS). They take just 50 samples from the WVS and use them to create more training data that’s similar in meaning. This helps computers learn about different cultures without needing lots of data or powerful computers. The results show that this approach works well, even better than some existing methods. |
Keywords
* Artificial intelligence * Data augmentation * Gemini * Gpt * Prompt
