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Pre-training Techniques for Language Models and Their Impact on Downstream Tasks

发表时间:2023-05-04 18:28

Pre-training Techniques for Language Models and Their Impact on Downstream Tasks

Abstract

Language models are an essential technology in natural language processing (NLP), and their main task is to predict the next word or character in a sentence or text sequence. Traditional language models are based on N-gram models and neural network models, but these models often have some shortcomings, such as requiring large amounts of annotated data and poor generalization ability. With the development of deep learning technology in recent years, language models based on pre-training techniques have gradually become mainstream.

Pre-training techniques can learn richer language representations by training on large-scale unlabeled corpora, and can be used in downstream tasks, including text classification, machine translation, sentiment analysis, and question-answering systems. Therefore, studying pre-training techniques is of great significance and value for improving the performance of NLP systems, reducing the cost of annotated data, and speeding up model training.

This paper aims to explore the pre-training techniques of language models and their impact on downstream tasks. By analyzing the applications of pre-training techniques in different downstream tasks, we will investigate the advantages, disadvantages, and influencing factors of pre-training techniques in different situations, with the goal of providing valuable conclusions and recommendations.

Keywords

language model, pre-training techniques, downstream tasks, NLP

Table of Contents:

Chapter 1: Introduction

1.1 Research Background and Significance

1.2 Research Objectives and Significance

1.3 Research Status and Development Trends

Chapter 2: Overview of Pre-training Techniques for Language Models

2.1 Deficiencies of Traditional Language Models

2.2 Basic Principles of Pre-training Techniques

2.3 Common Pre-training Models and Methods

Chapter 3: Applications of Pre-training Techniques in Downstream Tasks

3.1 Text Classification

3.2 Machine Translation

3.3 Sentiment Analysis

3.4 Text Generation

Chapter 4: Analysis of Influencing Factors of Pre-training Techniques

4.1 Training Data Scale

4.2 Training Objectives and Loss Functions

4.3 Structure and Hyperparameters of Pre-training Models

Chapter 5: Experimental Design and Result Analysis

5.1 Introduction of Experimental Design and Datasets

5.2 Experimental Results and Performance Analysis

5.3 Discussion and Analysis of Results

Chapter 6: Conclusion and Future Work

6.1 Main Conclusions of the Thesis

6.2 Shortcomings and Prospects

6.3 Significance and Value of Research

Chapter 7: References



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