👉 Alignment math is a fundamental concept in natural language processing (NLP) that quantifies the similarity between an input text and a set of predefined, contextually relevant sentences, known as alignment models. It typically uses a distance metric, such as cosine similarity or Euclidean distance, to measure how closely the input text matches each reference sentence in the alignment model. The alignment score is then calculated as the inverse of this distance, ensuring that sentences with higher similarity to the reference sentences receive higher scores. This process helps in tasks like machine translation, question answering, and summarization by identifying the most relevant parts of the input text to the given context or question. The alignment model can be trained using supervised learning techniques, where it learns to map input sentences to their corresponding reference sentences based on labeled data.
