A 1B LLaMA model fine-tuned with LoRA on 100-1000 synthetic samples shows high ROUGE-L and JSON parse rates on three extraction tasks, but the comparison against zero-shot 7B/8B models does not support the claim that small models outperform large ones.
Advancing entity recognition in biomedicine via instruction-based approaches,
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Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model
A 1B LLaMA model fine-tuned with LoRA on 100-1000 synthetic samples shows high ROUGE-L and JSON parse rates on three extraction tasks, but the comparison against zero-shot 7B/8B models does not support the claim that small models outperform large ones.