Summary of Towards Linguistically-aware and Language-independent Tokenization For Large Language Models (llms), by Abrar Rahman et al.
Towards Linguistically-Aware and Language-Independent Tokenization for Large Language Models (LLMs)
by Abrar Rahman, Garry Bowlin, Binit Mohanty, Sean McGunigal
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 study examines the tokenization techniques used by advanced language models, such as GPT-4, GPT-3, DaVinci, and BERT base, to understand their impact on service availability and cost across various languages. The research focuses on low-resource languages and investigates the variability in subword tokenization among these models. Additionally, the paper highlights the importance of linguistically-aware development practices, particularly for traditionally under-resourced languages. Case studies illustrate the real-world implications of tokenization choices in electronic health record systems. This study aims to promote inclusive internationalization (I18N) practices in AI service development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how big language models work and how they affect people who don’t speak English well or have limited access to technology. The study compares different models, like GPT-4 and BERT, to see how they represent words from around the world. It also talks about how important it is to make sure that AI systems are designed with languages other than English in mind. This helps people who don’t speak English well or have limited access to technology use these systems equally well. |
Keywords
» Artificial intelligence » Bert » Gpt » Tokenization
