"Small Language Models (SLMs) Can Still Pack a Punch: A survey"

This survey shows that small language models (1-8B parameters) can match or outperform larger models.

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https://arxiv.org/abs/2501.05465

🛠️ Methods in this Paper:

→ The paper surveys 160 research works showcasing Small Language Models (SLMs) in the 1-8B parameter range.

→ It categorizes SLMs into task-agnostic models like Llama2 and Mistral, and task-specific ones for math, code, and translation.

→ Key techniques include knowledge distillation, progressive learning, and explanation tuning.

→ Post-training optimizations like quantization and pruning make models more efficient.

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💡 Key Insights:

→ High-quality training data matters more than model size

→ Task-specific SLMs often outperform larger general models

→ Innovative architectures like hybrid state space models boost efficiency

→ SLMs are practical for resource-constrained environments