Researchers From Columbia University And Databricks Conducted A Comparative Study Of Lora And Full Finetuning In Large Language Models

Researchers have explored efficient methods for fine-tuning large language models (LLMs) to improve their performance while minimizing computational resources. One such method, Low-Rank Adaptation (LoRA), aims to save memory by only modifying a small subset of parameters. However, its effectiveness compared to full finetuning has been debated, especially in challenging domains. A recent study compared the performance of LoRA and full finetuning in two target domains, finding that LoRA can achieve comparable performance with significantly less computational cost.