Summary of Evaluating Tokenizer Performance Of Large Language Models Across Official Indian Languages, by S. Tamang and D. J. Bora
Evaluating Tokenizer Performance of Large Language Models Across Official Indian Languages
by S. Tamang, D. J. Bora
First submitted to arxiv on: 19 Nov 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 This paper evaluates the tokenization processes used by 12 Large Language Models (LLMs) across all 22 official languages of India. The authors employed the Normalized Sequence Length (NSL) as a key metric to compare the efficiency of these tokenizers. The study reveals that the SUTRA tokenizer outperforms other models, including several Indic-specific models, excelling in 14 languages. Notably, GPT-4o shows improvement over its predecessor GPT-4 in processing Indian languages, while Project Indus displays limited performance in certain languages. The findings emphasize the importance of developing targeted tokenization strategies for multilingual and Indic-centric models to enhance linguistic coverage and model efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well different computer programs can understand and process text from many languages, including those spoken in India. They compared 12 special language models that can learn new things and remember them. The goal was to find the best way for these models to prepare text for processing. The results show that one model, called SUTRA, does better than others at understanding languages like Hindi, Bengali, and more. This study is important because it helps us understand how to make language learning programs work better with many different languages. |
Keywords
» Artificial intelligence » Gpt » Tokenization » Tokenizer
