Building pre-train LLM Dataset for the INDIC Languages: a case study on Hindi
This provides a foundational resource for building LLMs in Hindi and other Indic languages, which is incremental but crucial for advancing NLP in these under-resourced contexts.
The paper addresses the lack of high-quality pre-training data for large language models (LLMs) in Indic languages by creating a dataset of 1.28 billion Hindi tokens collected from multiple domains and dialects, with a pipeline designed for easy extension to other low-resource languages.
Large language models (LLMs) demonstrated transformative capabilities in many applications that require automatically generating responses based on human instruction. However, the major challenge for building LLMs, particularly in Indic languages, is the availability of high-quality data for building foundation LLMs. In this paper, we are proposing a large pre-train dataset in Hindi useful for the Indic language Hindi. We have collected the data span across several domains including major dialects in Hindi. The dataset contains 1.28 billion Hindi tokens. We have explained our pipeline including data collection, pre-processing, and availability for LLM pre-training. The proposed approach can be easily extended to other Indic and low-resource languages and will be available freely for LLM pre-training and LLM research purposes.