How Bag of Words Works – The Foundation of Language Models

The Bag of Words (BoW) model, which transforms text into word-count vectors, laid the critical foundation for modern language models. By converting sentences into numerical data where only word frequency matters (ignoring grammar and context), BoW enabled computers to process human language mathematically despite limitations in capturing meaning. Though simpler than today's advanced models like BERT and GPT, this breakthrough approach first bridged the gap between words and numbers, making possible all subsequent advancements in natural language processing.