Position Engineering

Position engineering is a burgeoning field focused on optimizing the performance of large language models (LLMs) and other machine learning models by manipulating their input data, specifically positional information, rather than modifying the content itself. Current research explores this technique across diverse applications, including image clustering (using attention mechanisms), genotype-phenotype prediction (leveraging LLMs for feature selection), and object tracking (employing transformer-based filters). This approach offers a potentially more efficient and effective way to enhance model capabilities, impacting various fields from healthcare and engineering to education and software development by improving accuracy, efficiency, and interpretability.