AI research

A research that learning through large-scale prompts is more effective than expensive fine-tuning methods. The recent advancements in large language models (LLMs) offer increased context capacity, highlighting the efficacy of in-context learning (ICL) approaches for application developers. While BM25 performs well in keyword-based searches for new questions, it struggles with many examples. Conversely, longer prompts with more examples enhance the power of LLMs, where ICL utilizes examples to solve problems rather than learning. Long examples make ICL more efficient than fine-tuning. Experimental results using LLAMA-2-7B and Mistral-7B models demonstrate smoother processing with larger context models. ICL enables the creation of services at a lower cost compared to fine-tuning, though mixing both methods may yield better outcomes, considering the a cost of fine-tuning versus increased compute resource usage with longer contexts.