Summary of Double Jeopardy and Climate Impact in the Use Of Large Language Models: Socio-economic Disparities and Reduced Utility For Non-english Speakers, by Aivin V. Solatorio et al.
Double Jeopardy and Climate Impact in the Use of Large Language Models: Socio-economic Disparities and Reduced Utility for Non-English Speakers
by Aivin V. Solatorio, Gabriel Stefanini Vicente, Holly Krambeck, Olivier Dupriez
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); General Economics (econ.GN)
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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 language gaps and information inequalities resulting from large language models (LLMs), particularly OpenAI’s GPT, as they process input through tokenization. The study finds that speakers of low-income languages face higher costs, 4-6 times those faced by English speakers, due to tokenization-based pricing. Moreover, LLMs exhibit poor performance in low-resource languages, presenting a “double jeopardy” for these users. The research highlights the need for fairer algorithm development to benefit all linguistic groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can help or hurt people who don’t speak English well. It finds that those who speak less common languages have to pay more to use these models because of how they process words. This makes it even harder for people in poorer countries to access information and communicate effectively. The research shows that the models don’t work very well for these languages, making things worse. Overall, this paper wants us to think about how we can make these language models fairer so everyone can benefit. |
Keywords
» Artificial intelligence » Gpt » Tokenization
