Pretraining Data and Tokenizer for Indic LLM

• URL: http://arxiv.org/abs/2407.12481v1
• Date: Wed, 17 Jul 2024 11:06:27 GMT
• Title: Pretraining Data and Tokenizer for Indic LLM
• Authors: Rahul Kumar, Shubham Kakde, Divyansh Rajput, Daud Ibrahim, Rishabh Nahata, Pidathala Sowjanya, Deepak Kumar,
• Abstract summary: We develop a novel approach to data preparation for developing multilingual Indic large language model. Our meticulous data acquisition spans open-source and proprietary sources, including Common Crawl, Indic books, news articles, and Wikipedia. For each Indic language, we design a custom preprocessing pipeline to effectively eliminate redundant and low-quality text content.
• Score: 1.7729311045335219
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel approach to data preparation for developing multilingual Indic large language model. Our meticulous data acquisition spans open-source and proprietary sources, including Common Crawl, Indic books, news articles, and Wikipedia, ensuring a diverse and rich linguistic representation. For each Indic language, we design a custom preprocessing pipeline to effectively eliminate redundant and low-quality text content. Additionally, we perform deduplication on Common Crawl data to address the redundancy present in 70% of the crawled web pages. This study focuses on developing high-quality data, optimizing tokenization for our multilingual dataset for Indic large language models with 3B and 7B parameters, engineered for superior performance in Indic languages. We introduce a novel multilingual tokenizer training strategy, demonstrating our custom-trained Indic tokenizer outperforms the state-of-the-art OpenAI Tiktoken tokenizer, achieving a superior token-to-word ratio for Indic languages.

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