Summary of Cultural Fidelity in Large-language Models: An Evaluation Of Online Language Resources As a Driver Of Model Performance in Value Representation, by Sharif Kazemi et al.
Cultural Fidelity in Large-Language Models: An Evaluation of Online Language Resources as a Driver of Model Performance in Value Representation
by Sharif Kazemi, Gloria Gerhardt, Jonty Katz, Caroline Ida Kuria, Estelle Pan, Umang Prabhakar
First submitted to arxiv on: 14 Oct 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 analyzes how large language models (LLMs) are trained with societal values from specific languages, affecting their ability to reflect those values. The study found a strong correlation between LLM performance and the availability of digital resources in that language. For example, GPT-4o’s accuracy was significantly higher for languages with more abundant online data. The research highlights the impact of language-specific training on LLMs’ performance, particularly in low-resource languages like those spoken in the Global South. To mitigate this digital divide, the authors propose developing multilingual LLMs from scratch and fine-tuning them on diverse linguistic datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how big computer models learn about different cultures by training with words and ideas from those cultures. They found that these models get better at understanding cultural values when they’re trained with lots of data in that language. For example, a model might do better with Japanese if it’s been trained on many Japanese websites. The study also shows how some countries have less online information in their languages than others, making it harder for the computer models to learn about those cultures. This could make people from those countries feel left out of the digital world. |
Keywords
» Artificial intelligence » Fine tuning » Gpt
