The Evolution of NLP Technology: A Practical Exploration from Bag-of-Words to Large Language Models

This GitHub repository (nlp_TPs) records the complete evolution trajectory of natural language processing (NLP) technology from traditional methods to modern deep learning, helping learners understand the core context through practical projects. The project covers stages such as Bag-of-Words model, TF-IDF, word embeddings, sequence models, Transformer, pre-trained models, and large language models (LLM), providing learners with a historical perspective and practical comparisons.

Original Author/Maintainer: Agustin-Wencelblat Source Platform: GitHub Original Title: nlp_TPs Original Link: https://github.com/Agustin-Wencelblat/nlp_TPs Release Date: 2026-05-31

Through a series of experiments and implementations, this project demonstrates the development trajectory of the NLP field from traditional methods to modern technologies, providing learners with practical cases to understand technology evolution.

Bag-of-Words Model (BoW)

Core: Treat text as a collection of words, count word frequencies to build a vector space model; Limitations: Loses word order, no semantic relationships, high-dimensional sparsity.

TF-IDF

Improvement: Introduce inverse document frequency to reduce the weight of common words, highlight feature words, and improve information retrieval and classification effects.

Word Embeddings

Breakthrough: Map words to low-dimensional dense vectors, capture semantic relationships (e.g., "king - man + woman ≈ queen"), support vector operations; Implementations include Skip-gram, CBOW, and negative sampling optimization.

Sequence Models (RNN/LSTM/GRU)

Features: Process variable-length sequences, transfer context information; LSTM solves long-distance dependency; Limitations: Serial computation, information attenuation.

Transformer Architecture

Innovation: Self-attention mechanism models relationships between any positions, parallel computation, multi-head attention captures multi-dimensional semantics, positional encoding preserves sequence information; Becomes the foundation for pre-trained models like BERT and GPT.

Pre-trained Models (BERT/GPT)

Paradigm: Pre-training + fine-tuning; BERT's bidirectional encoder is suitable for understanding tasks, while GPT's unidirectional decoder is suitable for generation tasks.

Large Language Models (LLM)

Features: Billions/trillions of parameters, emergent context learning and reasoning abilities, support zero-shot/few-shot learning, and unified multi-task processing.

This project provides learners with:

• Historical Perspective: Understand the evolution path of technology and the driving force of core problems;
• Comparative Learning: Intuitively feel the advantages and disadvantages of each method;
• Solid Foundation: Modern large models rely on basic technologies such as word embeddings and attention;
• Critical Thinking: Understand the limitations of technology and avoid blind pursuit of the latest methods.

Although LLM is mainstream, early technologies still have practical significance:

• Resource-Constrained Scenarios: Use BoW/TF-IDF on mobile/edge devices;
• Interpretability Requirements: Traditional methods are more transparent, suitable for fields such as finance and medical care;
• Specific Task Optimization: Traditional methods are more efficient for simple tasks;
• Model Understanding: Mastering the attention mechanism helps debug large models.

The nlp_TPs project presents a complete picture of NLP technology evolution. Each generation of technology solves the limitations of the previous generation but brings new challenges. Understanding the evolution process helps master technology and gain trend insight.

Recommendations: Beginners should practice according to the timeline to experience the characteristics of each stage; experienced practitioners should review the basics to deepen their understanding of the essence of large models.