Efficient Continual Pre-training of LLMs for Low-resource Languages

• URL: http://arxiv.org/abs/2412.10244v1
• Date: Fri, 13 Dec 2024 16:13:35 GMT
• Title: Efficient Continual Pre-training of LLMs for Low-resource Languages
• Authors: Arijit Nag, Soumen Chakrabarti, Animesh Mukherjee, Niloy Ganguly,
• Abstract summary: We develop a new algorithm to select a subset of texts from a larger corpus. In search of further improvement, we design a new algorithm to select tokens to include in the LLM vocabulary.
• Score: 45.44796295841526
• License:
• Abstract: Open-source Large Language models (OsLLMs) propel the democratization of natural language research by giving the flexibility to augment or update model parameters for performance improvement. Nevertheless, like proprietary LLMs, Os-LLMs offer poorer performance on low-resource languages (LRLs) than high-resource languages (HRLs), owing to smaller amounts of training data and underrepresented vocabulary. On the other hand, continual pre-training (CPT) with large amounts of language-specific data is a costly proposition in terms of data acquisition and computational resources. Our goal is to drastically reduce CPT cost. To that end, we first develop a new algorithm to select a subset of texts from a larger corpus. We show the effectiveness of our technique using very little CPT data. In search of further improvement, we design a new algorithm to select tokens to include in the LLM vocabulary. We experiment with the recent Llama-3 model and nine Indian languages with diverse scripts and extent of resource availability. For evaluation, we use IndicGenBench, a generation task benchmark dataset for Indic languages. We experiment with various CPT corpora and augmented vocabulary size and offer insights across language families.

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