Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks

"Fine-tuning versus in-context learning (ICL) continues to be a hot topic as researchers seek optimal ways to enhance large language models. Fine-tuning involves costly retraining on new data but lacks strong generalization capabilities, while ICL offers flexible context guidance, excelling in structured tasks like logical reasoning but requiring careful prompt design. A hybrid approach combining fine-tuned data with ICL-generated examples showed promise, improving model performance without significantly increasing computational demands."