$M^3$ Scaling Law: Optimizing Multi-Epoch, Multi-Lingual, and Multi-Stage Training for Low-Resource Language Models
arXiv:2410.12325v2 Announce Type: replace Abstract: In this paper, we study a fundamental design problem in pretraining Large Language Models (LLMs) for low-resource language regimes. Existing works adopt multi-epoch, multi-lingual, and multi-stage training to utilize the limited target-language corpus efficiently, but no prior scaling law can compare recipes spanning these approaches under the same compute budget $C$ and target-language corpus size $D_T$, leaving the optimal training setup unclear. To address this gap, we propose the $M^3$ Scaling Law, a unified predictive model parameterized
The proliferation of LLMs creates an urgent need for efficient training methods, particularly for languages with limited data, driving research into optimization strategies like the M3 Scaling Law.
This research provides a framework for optimizing LLM training in low-resource environments, directly impacting global AI accessibility and the equitable development of AI capabilities beyond major languages.
The ability to more effectively train LLMs for low-resource languages could democratize AI development, reducing dependency on a few dominant linguistic datasets and enabling new applications in underserved markets.
- · Low-resource language communities
- · AI developers in emerging markets
- · Multilingual AI platforms
- · Researchers in LLM optimization
- · Companies relying solely on high-resource language data advantage
- · Monopolies in AI language model development
The M3 Scaling Law provides a unified model to optimize LLM training for low-resource languages by comparing different training 'recipes'.
Improved efficiency in training low-resource LLMs could accelerate their adoption and lead to the development of tailored AI solutions for diverse linguistic and cultural contexts.
Enhanced AI capabilities in low-resource languages could foster greater digital inclusion and potentially shift geopolitical power dynamics in AI development, reducing the dominance of a few tech hubs.
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