Small Number

Research on utilizing small numbers of data points or parameters in various machine learning and optimization tasks is gaining traction, driven by limitations in data acquisition, computational resources, or the need for explainability. Current efforts focus on developing algorithms and model architectures that maintain accuracy despite this constraint, including adaptations of existing methods like k-means and CMA-ES, and novel approaches such as low-rank adaptation (LoRA-XS) for parameter-efficient fine-tuning of large language models. This research is significant because it addresses practical challenges in diverse fields, from industrial quality control and leak detection to medical diagnosis and scientific computing, enabling efficient and effective solutions where large datasets or extensive computational power are unavailable.