Fine-Tuning LLMs: Selecting the Optimal Supervised Approach

Large language models (LLMs) are potent tools that mimic human text generation, comprehend intricate queries, and execute diverse language tasks. However, developing them from scratch can be expensive and time-intensive. A more efficient approach is supervised fine-tuning, refining existing LLMs swiftly for specific tasks or domains.