👉 Dice engineering is an advanced technique in machine learning, particularly within the realm of natural language processing (NLP), that focuses on refining and enhancing the semantic alignment between text and numerical representations, or embeddings. It involves creating custom probability distributions over a vocabulary to better capture the nuanced relationships and contextual meanings of words, phrases, or sentences. By doing so, dice engineering aims to improve the performance of models in tasks such as text classification, sentiment analysis, and machine translation by ensuring that the model's embeddings more accurately reflect the true semantic similarity between different pieces of text. This is achieved through various methods, including training on large, high-quality datasets, applying statistical models to estimate the overlap between true and predicted embeddings, and iteratively refining these distributions to minimize discrepancies. The goal of dice engineering is to produce embeddings that are more discriminative and contextually aware, leading to more robust and accurate NLP applications.
