Task Specific Knowledge

Task-specific knowledge focuses on efficiently equipping AI models with the information needed to excel at particular tasks, minimizing the need for extensive retraining or reliance on large, general-purpose models. Current research emphasizes methods for incorporating this knowledge, including prompt engineering, parameter-efficient fine-tuning (like LoRA and adapters), and knowledge distillation from large language models (LLMs). These advancements aim to improve model performance, reduce computational costs, and enhance adaptability to new tasks, impacting fields like natural language processing, computer vision, and robotics.