Full analysis of NLP text generation: a complete introduction from traditional methods to pre-training

This article takes an in-depth look at various methods of text generation, from traditional statistical and template-based techniques to modern neural network models, especially LSTM and Transformer architectures. The article also details the application of large-scale pre-training models such as GPT in text generation, and provides implementation codes for Python and PyTorch.

Follow TechLead and share all-dimensional knowledge of AI. The author has 10+ years of Internet service architecture, AI product research and development experience, and team management experience. He holds a master's degree from Tongji University in Fudan University, a member of Fudan Robot Intelligence Laboratory, a senior architect certified by Alibaba Cloud, a project management professional, and research and development of AI products with revenue of hundreds of millions. principal.

1 Introduction

1.1 Definition and function of text generation

Text generation is a core subfield of natural language processing that involves using models to automatically create natural language text. This generation can be a response based on some input, such as an image or other text, or it can be a completely autonomous creation.

Text generation tasks can be as simple as automatically replying to an email, or more complex as writing a news article or generating a story. It usually includes the following steps:

1. Determine goals and constraints : Clarify the goals and constraints for generating text, such as style, language, and length.
2. Content generation : Generate content based on predefined goals and constraints.
3. Evaluation and optimization : Use different evaluation metrics to test the generated text and make necessary optimizations.

example:

• Auto-reply emails : Based on the content of the received email, the system can generate a short, relevant reply.
• News article generation : Use existing data and information to automatically generate news articles.
• Story Generation : Create a system that generates stories based on input prompts.

1.2 Use of natural language processing technology in the field of text generation

Natural language processing technology provides powerful tools and methods for text generation. These techniques can be used to parse input data, understand language structure, assess the quality of generated text, and optimize the generation process.

1. Sequence-to-sequence model : This is a framework widely used in text generation tasks such as machine translation and summary generation. The model learns to transform an input sequence (such as a sentence) into an output sequence (such as a sentence in another language).

2. Attention mechanism : When processing long sequences, the attention mechanism can help the model focus on key parts of the input data, thereby producing more accurate output.

3. Pre-trained language models : Models like BERT and GPT are pre-trained on large amounts of text data and can later be used for various NLP tasks, including text generation.

4. Optimization techniques : such as beam search and sampling strategies, which can help produce smoother, more accurate text.

example:

• Machine Translation : Use sequence-to-sequence models to convert English sentences into French sentences.
• Generate summary : Use the attention mechanism to extract key information from long articles and generate short summaries.
• Text Filling : Use a pre-trained GPT model to generate a complete story based on a given beginning.

With the advancement of technology, the application of natural language processing technology in text generation is becoming more and more extensive, providing us with more possibilities and opportunities.

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2 Traditional methods - statistical based methods

Before the popularity of deep learning technology, text generation mainly relied on statistics-based methods. These methods predict the occurrence probability of the next word or phrase by counting the frequency of words and phrases in the corpus.

2.1.1 N-gram model

Definition : The N-gram model is a classic technique in statistics-based text generation methods. It is based on the assumption that the occurrence of the Nth word is only related to the previous N-1 words. For example, in a trigram (3-gram) model, the occurrence of the next word is only related to the previous two words.

Example : Consider the sentence "I love learning artificial intelligence". In a bigram (2-gram) model, the next word after "artificial" may be "intelligence".

from collections import defaultdict, Counter
import random

def build_ngram_model(text, n=2):
    model = defaultdict(Counter)
    for i in range(len(text) - n):
        context, word = tuple(text[i:i+n-1]), text[i+n-1]
        model[context][word] += 1
    return model

def generate_with_ngram(model, max_len=20):
    context = random.choice(list(model.keys()))
    output = list(context)
    for i in range(max_len):
        if context not in model:
            break
        next_word = random.choices(list(model[context].keys()), weights=model[context].values())[0]
        output.append(next_word)
        context = tuple(output[-len(context):])
    return ' '.join(output)

text = "我 爱 学习 人工 智能".split()
model = build_ngram_model(text, n=2)
generated_text = generate_with_ngram(model)
print(generated_text)

2.1.2 Smoothing technology

Definition : In statistical models, we often encounter a problem that there may be some N-grams in the corpus that have never appeared, resulting in a probability of 0. To solve this problem, we use smoothing techniques to assign a non-zero probability to these non-appearance N-grams.

Example : Using Add-1 smoothing (Laplace smoothing), we add 1 to the count of each word to ensure that the probability of no word is 0.

def laplace_smoothed_probability(word, context, model, V):
    return (model[context][word] + 1) / (sum(model[context].values()) + V)

V = len(set(text))
context = ('我', '爱')
probability = laplace_smoothed_probability('学习', context, model, V)
print(f"P('学习'|'我 爱') = {
      
      probability}")

While we can generate text by using statistics-based methods, these methods have their limitations, especially when dealing with long texts. With the development of deep learning technology, more advanced models have gradually replaced traditional methods, bringing more possibilities to text generation.

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3. Traditional approach – template-based generation

Template-based text generation is an early text generation method that relies on predefined sentence structures and vocabulary to create text. Although this method is simple and intuitive, the text it generates often lacks variation and diversity.

3.1 Definition and characteristics

Definition : The template generation method involves using predefined text templates and fixed structures, filling these templates according to different data or context, thereby generating text.

Features :

1. Deterministic : The output is predictable because it is directly based on the template.
2. Fast generation : no complex calculations required, just simply fill in the template.
3. Limitations : The output may lack variety and natural feel because it is based entirely on a fixed template.

Example : In a weather forecast, you can have a template: "Today's highest temperature in {city} is {temperature} degrees.". Based on different data, we can fill the template and generate sentences such as "Today's highest temperature in Beijing is 25 degrees."

def template_generation(template, **kwargs):
    return template.format(**kwargs)

template = "今天在{city}的最高温度为{temperature}度。"
output = template_generation(template, city="北京", temperature=25)
print(output)

3.2 Dynamic templates

Definition : In order to increase the diversity of text, we can design multiple templates and select different templates to fill based on context or randomness.

Example : For weather forecast, we can have the following template:

1. "The temperature in {city} today reached {temperature} degrees."
2. "Today's highest temperature in {city} is {temperature} degrees."

import random

def dynamic_template_generation(templates, **kwargs):
    chosen_template = random.choice(templates)
    return chosen_template.format(**kwargs)

templates = [
    "{city}今天的温度达到了{temperature}度。",
    "在{city}，今天的最高气温是{temperature}度。"
]

output = dynamic_template_generation(templates, city="上海", temperature=28)
print(output)

Although the template-based approach provides a simple and straightforward way for text generation, it may prove inadequate when handling complex and diverse text generation tasks. Modern deep learning methods provide more powerful, flexible and diverse text generation capabilities and have gradually become mainstream methods.

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4. Neural network method - long short-term memory network (LSTM)

Long short-term memory network (LSTM) is a special type of recurrent neural network (RNN) designed to solve long-term dependency problems. In traditional RNN, as the time steps increase, the transmission of information gradually becomes difficult. LSTM solves this problem through its special structure, allowing information to flow more easily between time steps.

Core concepts of LSTM

Definition : The core of LSTM is its cell state, usually represented as (C_t). At the same time, LSTM contains three important gates: forgetting gate, input gate and output gate, which together determine how information is updated, stored and retrieved.

1. Forgetting Gate : Determines which information is forgotten or discarded from the cell state.
2. Input gate : updates the cell state and determines which new information is stored.
3. Output gate : Based on the cell state, decide what information to output.

Example : Suppose we are working with a text sequence and want to remember the gender marker of a word (such as "he" or "she"). When we encounter a new pronoun, the forgetting gate may help the model forget the old gender marker, the input gate will help the model store the new marker, and the output gate will output this marker at the next time step to keep the sequence consistent. sex.

LSTM in PyTorch

Using PyTorch, we can easily define and train an LSTM model.

import torch.nn as nn
import torch

# 定义LSTM模型
class LSTMModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
        super(LSTMModel, self).__init__()
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
        self.linear = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        # 初始化隐藏状态和细胞状态
        h0 = torch.zeros(num_layers, x.size(0), hidden_dim).requires_grad_()
        c0 = torch.zeros(num_layers, x.size(0), hidden_dim).requires_grad_()
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        out = self.linear(out[:, -1, :])
        return out

input_dim = 10
hidden_dim = 20
output_dim = 1
num_layers = 1
model = LSTMModel(input_dim, hidden_dim, output_dim, num_layers)

# 一个简单的例子，输入形状为 (batch_size, time_steps, input_dim)
input_seq = torch.randn(5, 10, 10)
output = model(input_seq)
print(output.shape)  # 输出形状为 (batch_size, output_dim)

LSTM has achieved remarkable success in a variety of natural language processing tasks, such as text generation, machine translation, and sentiment analysis due to its ability to process time series data, especially retaining key information in long sequences.

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5. Neural network method - Transformer

Transformer is an important development in the field of natural language processing in recent years. It abandons the traditional recursive and convolutional structures and relies entirely on the self-attention mechanism to process sequence data.

Core concepts of Transformer

Definition : Transformer is a deep learning model based on the self-attention mechanism, designed to process sequence data such as text. At its core is a multi-head self-attention mechanism that captures dependencies between different positions in a sequence, no matter how far apart they are.

Multi-Head Self-Attention : This is a key part of the Transformer. Each "head" learns a representation of a different position in the sequence and then combines these representations.

Positional encoding : Since the Transformer does not use recursion or convolution, additional positional information is needed to understand the position of the word in the sequence. Positional encoding adds this information to each position in the sequence.

Example : Consider the sentence "The cat sat on the mat." If we want to emphasize the relationship between "cat" and "mat", the multi-head self-attention mechanism allows the Transformer to pay attention to both "cat" and the distant "mat" at the same time. .

Transformer in PyTorch

Using PyTorch, we can use the ready-made Transformer module to define a simple Transformer model.

import torch.nn as nn
import torch

class TransformerModel(nn.Module):
    def __init__(self, d_model, nhead, num_encoder_layers, num_decoder_layers):
        super(TransformerModel, self).__init__()
        self.transformer = nn.Transformer(d_model, nhead, num_encoder_layers, num_decoder_layers)
        self.fc = nn.Linear(d_model, d_model)  # 示例中的一个简单的线性层

    def forward(self, src, tgt):
        output = self.transformer(src, tgt)
        return self.fc(output)

d_model = 512
nhead = 8
num_encoder_layers = 6
num_decoder_layers = 6

model = TransformerModel(d_model, nhead, num_encoder_layers, num_decoder_layers)

# 示例输入，形状为 (sequence_length, batch_size, d_model)
src = torch.randn(10, 32, d_model)
tgt = torch.randn(20, 32, d_model)

output = model(src, tgt)
print(output.shape)  # 输出形状为 (tgt_sequence_length, batch_size, d_model)

Transformer has achieved breakthrough results in a variety of natural language processing tasks due to its powerful self-attention mechanism and parallel processing capabilities. Models such as BERT, GPT, and T5 are all built based on the Transformer architecture.

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6. Large-scale pre-training model-GPT text generation mechanism

In recent years, large-scale pre-training models such as GPT, BERT, and T5 have become standard models in the field of natural language processing. They have demonstrated excellent performance on a variety of tasks, especially text generation tasks.

Core concepts of large pre-trained models

Definition : A large pre-trained model is a model that is pre-trained on a large amount of unlabeled data and then fine-tuned on specific tasks. This “pre-training-fine-tuning” paradigm enables the model to capture rich representations of natural language and provides a powerful starting point for a variety of downstream tasks.

Pre-training : The model performs unsupervised learning on large-scale text data, such as books, web pages, etc. At this point, the model has learned vocabulary, grammar and some common sense information.

Fine-tuning : After pre-training, the model performs supervised learning on labeled data for a specific task, such as machine translation, text generation, or sentiment analysis.

Example : Consider GPT-3, which is first pre-trained on a large amount of text to learn the basic structure and information of the language. Text can then be generated directly on a specific task with few samples or without any additional training.

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Follow TechLead and share all-dimensional knowledge of AI. The author has 10+ years of Internet service architecture, AI product research and development experience, and team management experience. He holds a master's degree from Tongji University in Fudan University, a member of Fudan Robot Intelligence Laboratory, a senior architect certified by Alibaba Cloud, a project management professional, and research and development of AI products with revenue of hundreds of millions. principal.