Summary of Responsible Multilingual Large Language Models: a Survey Of Development, Applications, and Societal Impact, by Junhua Liu and Bin Fu
Responsible Multilingual Large Language Models: A Survey of Development, Applications, and Societal Impact
by Junhua Liu, Bin Fu
First submitted to arxiv on: 23 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 presents a comprehensive framework for developing and deploying Multilingual Large Language Models (MLLMs) in production environments. It makes three distinct contributions: an actionable pipeline from data pre-processing to deployment, detailed optimization strategies using Llama2 as a case study, and an interdisciplinary analysis considering technical, linguistic, and cultural perspectives. The findings reveal critical challenges in supporting linguistic diversity, affecting over a billion speakers worldwide. Practical solutions are examined through real-world applications in customer service, search engines, and machine translation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps artificial intelligence become more accessible across different languages by providing guidelines for creating and using large language models that can understand many languages. It shows how to make these models better by learning from high-resource languages and low-resource languages, and by adjusting tokenization and sampling methods. The study also looks at the challenges of supporting linguistic diversity and provides real-world examples of how this technology can be used in applications like customer service and search engines. |
Keywords
» Artificial intelligence » Optimization » Tokenization » Translation
