What type of study is this?

This is a Literature Review study.

September 23, 2025Open Access

The Development of NLP Models

Key Points

The article outlines the transition from statistical models to modern neural network approaches in NLP, highlighting their strengths.
Experiments demonstrate the effectiveness of fine-tuning techniques like LoRA in enhancing natural language processing applications.
The review presents a detailed exploration of large language models, emphasizing their utility in various applications such as text classification.
It stresses the importance of iterative learning and practical workflows for effectively implementing NLP technologies.

Abstract

This article reviews the development of Natural Language Processing (NLP) models from early statistical approaches to modern large language models (LLMs). Beginning with probability-based n-gram models, it outlines their limitations in data sparsity and long-term dependency. It then introduces neural network-based models, including word embeddings, logistic regression, multi-layer perceptrons, and Word2Vec, followed by sequential models such as RNNs and LSTMs that capture temporal dependencies. The shift to pre-trained models, marked by Word2Vec and the Transformer architecture, enabled scalable transfer learning and laid the foundation for state-of-the-art models like BERT, GPT, and their derivatives. Applications in text classification are illustrated through experiments with RoBERTa, hybrid BERT-LightGBM models, and fine-tuning techniques such as LoRA. Finally, the article discusses the broader ecosystem of large models, including chat models, multimodal models, and agent frameworks that integrate planning, memory, and tool use. The review emphasizes both theoretical principles and practical workflows, highlighting the necessity of iterative learning, coding practice, and ecosystem familiarity for effectively leveraging NLP technologies.

The Development of NLP Models

Key Points

Abstract

Cite This Study