One article decoding language models: principles, practice and evaluation of language models

In this article, we take a deep dive into the inner workings of language models, from basic models to large-scale variants, and analyze the pros and cons of various evaluation metrics. The article provides a comprehensive and in-depth perspective through code examples, algorithm details, and latest research, aiming to help readers more accurately understand and evaluate the performance of language models. This article is suitable for researchers, developers, and readers who are interested in artificial intelligence.

Follow TechLead and share all-dimensional knowledge of AI. The author has more than 10 years of experience in Internet service architecture, AI product development, and team management. He is a Fudan master of Tongji, a member of Fudan Robot Intelligence Laboratory, a senior architect certified by Alibaba Cloud, a project management professional, and has hundreds of millions of revenue in AI product development. principal.

1. Overview of language model

What is a language model?

Language Model (LM for short) is a probabilistic model for modeling natural language (that is, the language people use every day). Simply put, the task of a language model is to evaluate the probability that a given sequence of words (ie, a sentence) will appear in the real world. This model plays a key role in many applications of natural language processing (NLP), such as machine translation, speech recognition, text generation, etc.

Core Concepts and Mathematical Representation

The language model attempts to model the probability distribution (P(w_1, w_2, \ldots, w_m)) of the word sequence (w_1, w_2, \ldots, w_m). Here, (w_i) is a word in the vocabulary (V), and (m) is the length of the sentence.

A basic requirement of this model is the normalization of the probability distribution, that is, the sum of the probabilities of all possible word sequences must equal 1:

Challenges: high dimensionality and sparsity

Imagine if we have a vocabulary of 10,000 words, there are (10,000^{20}) possible combinations for a sentence of 20 words, which is an astronomical number. Therefore, it is impractical to directly model such high dimensionality and sparsity.

The chain rule and conditional probability

In order to solve this problem, the Chain Rule is usually used to decompose the joint probability into the product of conditional probabilities:

Example

Suppose we have a sentence "I love language models". The chain rule allows us to calculate its probability like this:

In this way, the model can estimate probabilities more efficiently.

Application scenarios

• Machine Translation : When generating a target language sentence, a language model is used to evaluate which sequence of words is more "natural".
• Speech recognition : Likewise, language models can be used to select the most likely one from multiple possible transcriptions.
• Text summarization : The generated summary needs to be grammatically correct and natural, which also relies on language models.

summary

In general, language model is a basic component in natural language processing, which can effectively simulate the complex structure and generation rules of natural language. Despite the challenges of high dimensionality and sparsity, language models have been able to achieve remarkable results in several NLP applications through various strategies and optimizations, such as chain rule and conditional probability.

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2. n-gram Language Models

basic concept

When faced with the high dimensionality and sparsity problems of language model probability distribution calculation, n-gram models (n-gram models) are a classic solution. n-gram language models simplify the model by limiting the number of historical words considered in the conditional probabilities. Specifically, it only considers the nearest (n-1) words to predict the next word.

mathematical representation

The chain rule is approximated according to the n-gram method as:

[
P(w_1, w_2, \ldots, w_m) \approx \prod_{i=1}^{m} P(w_i | w_{i-(n-1)}, w_{i-(n-2)}, \ldots, w_{i-1})
]

Among them, (n) is the "order" of the model, usually an integer less than or equal to 5.

Code example: Calculate Bigram probability

Below is a simple example of a Bigram (2-gram) language model implemented in Python and underlying data structures.

from collections import defaultdict, Counter

# 训练文本，简化版
text = "I love language models and I love coding".split()

# 初始化
bigrams = list(zip(text[:-1], text[1:]))
bigram_freq = Counter(bigrams)
unigram_freq = Counter(text)

# 计算条件概率
def bigram_probability(word1, word2):
    return bigram_freq[(word1, word2)] / unigram_freq[word1]

# 输出
print("Bigram Probability of ('love', 'language'):", bigram_probability('love', 'language'))
print("Bigram Probability of ('I', 'love'):", bigram_probability('I', 'love'))

input and output

• Input : A set of words separated by spaces, representing the training text.
• Output : Bigram conditional probability of the formation of two specific words (such as 'love' and 'language').

Run the above code and you should see the following output:

Bigram Probability of ('love', 'language'): 0.5
Bigram Probability of ('I', 'love'): 1.0

Advantages and Disadvantages

advantage

1. The calculation is simple : the model parameters are easy to estimate, and only word frequencies need to be counted.
2. Space efficiency : Compared with the full sequence model, the n-gram model needs to store a much smaller number of parameters.

shortcoming

1. Data sparseness : For low-frequency or non-occurring n-grams, the model cannot give suitable probability estimates.
2. Limitations : Only local (n-1 word window) word dependencies can be captured.

summary

The n-gram language model simplifies the calculation of the probability distribution through local approximation, thus solving some problems of high dimensionality and sparsity. However, this also brings new challenges, such as how to deal with sparse data. Next, we introduce neural network-based language models that are able to deal with these challenges more effectively.

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3. Neural Network Language Models

basic concept

The neural network language model (NNLM) attempts to use deep learning to solve the data sparsity and limitations of traditional n-gram models. NNLM uses word embeddings (word embeddings) to capture the semantic information between words and calculate the conditional probability of words through neural networks.

mathematical representation

For a given word sequence (w_1, w_2, \ldots, w_m), NNLM tries to calculate:

[
P(w_m | w_{m-(n-1)}, \ldots, w_{m-1}) = \text{Softmax}(f(w_{m-(n-1)}, \ldots, w_{m-1}; \theta))
]

Among them, (f) is a neural network function, (\theta) is the model parameter, and Softmax is used to convert the output into probability.

Code Example: Simple NNLM

The following is a code example of a simple NNLM implemented using PyTorch.

import torch
import torch.nn as nn
import torch.optim as optim

# 数据准备
vocab = {
    
    "I": 0, "love": 1, "coding": 2, "<PAD>": 3}  # 简化词汇表
data = [0, 1, 2]  # "I love coding" 的词ID序列
data = torch.LongTensor(data)

# 参数设置
embedding_dim = 10
hidden_dim = 8
vocab_size = len(vocab)

# 定义模型
class SimpleNNLM(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(SimpleNNLM, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.rnn = nn.RNN(embedding_dim, hidden_dim)
        self.fc = nn.Linear(hidden_dim, vocab_size)

    def forward(self, x):
        x = self.embedding(x)
        out, _ = self.rnn(x.view(len(x), 1, -1))
        out = self.fc(out.view(len(x), -1))
        return out

# 初始化模型与优化器
model = SimpleNNLM(vocab_size, embedding_dim, hidden_dim)
optimizer = optim.SGD(model.parameters(), lr=0.1)

# 训练模型
for epoch in range(100):
    model.zero_grad()
    output = model(data[:-1])
    loss = nn.CrossEntropyLoss()(output, data[1:])
    loss.backward()
    optimizer.step()

# 预测
with torch.no_grad():
    prediction = model(data[:-1]).argmax(dim=1)
    print("Predicted words index:", prediction.tolist())

input and output

• Input : A sequence of words, each represented by its index in the vocabulary.
• Output : The predicted index of the next word, calculated by the model.

Running the above code, the output might be:

Predicted words index: [1, 2]

This means that the model predicts that "love" will be followed by "coding".

Advantages and Disadvantages

advantage

1. Capture long-range dependencies : Through looping or self-attention mechanisms, the model can capture longer-range dependencies.
2. Shared representation : word embeddings can be reused in different contexts.

shortcoming

1. Computational complexity : Compared with n-gram, NNLM has higher computational cost.
2. Data requirements : Deep models usually require large amounts of labeled data for training.

summary

The neural network language model significantly improves the expressiveness and accuracy of the language model by utilizing deep neural networks and word embeddings. However, this increased power comes at the cost of computational complexity. In the next section, we will explore how to further improve model performance through pre-training.

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Train language model

In the field of natural language processing, methods based on pre-trained language models have gradually become mainstream. From ELMo to GPT to BERT and BART, pre-trained language models perform well on multiple NLP tasks. In this section, we discuss how to train language models in detail, while also exploring various model structures and training tasks.

Pre-training and fine-tuning

Influenced by the use of ImageNet in the field of computer vision to conduct a pre-selection training of the model, the paradigm of pre-training + fine-tuning has also been widely used in the field of NLP. Pretrained models can be used for multiple downstream tasks, often requiring only fine-tuning.

ELMo: Dynamic word vector model

ELMo uses a bidirectional LSTM to generate word vectors. The vector representation of each word depends on the entire input sentence and is therefore "dynamic".

GPT: Generative pre-trained model

OpenAI's GPT uses a generative pre-training method and Transformer structure. It is characterized by a one-way model that can only model text sequences from left to right or right to left.

BERT: Bidirectional pre-training model

BERT uses the Transformer encoder and masking mechanism to further mine the rich semantics brought by the context. During pre-training, BERT uses two tasks: Masked Language Model (MLM) and Next Sentence Prediction (NSP).

BART: Bidirectional and Autoregressive Transformer

BART combines the bidirectional contextual information of BERT and the autoregressive properties of GPT, suitable for generation tasks. The pre-training tasks include denoising autoencoders, which introduce noise on the input text in a variety of ways.

Code Example: Training a Simple Language Model Using PyTorch

The code below shows how to use the PyTorch library to train a simple RNN language model.

import torch
import torch.nn as nn
import torch.optim as optim

# 初始化模型
class RNNModel(nn.Module):
    def __init__(self, vocab_size, embed_size, hidden_size):
        super(RNNModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_size)
        self.rnn = nn.RNN(embed_size, hidden_size)
        self.decoder = nn.Linear(hidden_size, vocab_size)

    def forward(self, x, h):
        x = self.embedding(x)
        out, h = self.rnn(x, h)
        out = self.decoder(out)
        return out, h

vocab_size = 1000
embed_size = 128
hidden_size = 256
model = RNNModel(vocab_size, embed_size, hidden_size)

# 损失和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(10):
    # 输入与标签
    input_data = torch.randint(0, vocab_size, (5, 32))  # 随机生成(序列长度, 批量大小)的输入
    target_data = torch.randint(0, vocab_size, (5, 32))  # 随机生成标签
    hidden = torch.zeros(1, 32, hidden_size)

    optimizer.zero_grad()
    output, hidden = model(input_data, hidden)
    loss = criterion(output.view(-1, vocab_size), target_data.view(-1))
    loss.backward()
    optimizer.step()

    print(f"Epoch [{
      
      epoch+1}/10], Loss: {
      
      loss.item():.4f}")

output

Epoch [1/10], Loss: 6.9089
Epoch [2/10], Loss: 6.5990
...

With this simple example, you can see that the input is a tensor of random integers representing vocabulary indices, and the output is a probability distribution that predicts the likelihood of the next word.

summary

Pretrained language models change many aspects of NLP. Through various structures and pre-training tasks, these models are able to capture rich semantic and contextual information. In addition, fine-tuning the pre-trained model is relatively simple and can be quickly adapted to various downstream tasks.

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large-scale language model

In recent years, large-scale pre-trained language models (Pre-trained Language Models, PLM) have played a revolutionary role in the field of natural language processing (NLP). This wave is led by models such as ELMo, GPT, and BERT, and it is still continuing today. This article aims to comprehensively and deeply explore the core principles of these models, including their structural design, pre-training tasks, and how to use them for downstream tasks. We'll also provide code examples for a deeper understanding.

ELMo: The Forerunner of Dynamic Word Embeddings

The ELMo (Embeddings from Language Models) model introduces the concept of contextualized word embeddings for the first time. Unlike traditional static word embeddings, dynamic word embeddings can dynamically adjust word embeddings based on context.

Code example: Word embedding using ELMo

# 用于ELMo词嵌入的Python代码示例
from allennlp.modules.elmo import Elmo, batch_to_ids

options_file = "https://allennlp.s3.amazonaws.com/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_options.json"
weight_file = "https://allennlp.s3.amazonaws.com/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5"

# 创建模型
elmo = Elmo(options_file, weight_file, 1, dropout=0)

# 将句子转换为字符id
sentences = [["I", "ate", "an", "apple"], ["I", "ate", "a", "carrot"]]
character_ids = batch_to_ids(sentences)

# 计算嵌入
embeddings = elmo(character_ids)

# 输出嵌入张量的形状
print(embeddings['elmo_representations'][0].shape)
# Output: torch.Size([2, 4, 1024])

GPT: Generative pre-trained model

GPT (Generative Pre-trained Transformer) adopts a generative pre-training method and is a unidirectional model based on Transformer architecture. This means it can only consider one side of the text context when processing the input text.

Code example: Generating text using GPT-2

# 使用GPT-2生成文本的Python代码示例
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

# 编码文本输入
input_text = "Once upon a time,"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

# 生成文本
with torch.no_grad():
    output = model.generate(input_ids, max_length=50)
    
# 解码生成的文本
output_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(output_text)
# Output: Once upon a time, there was a young prince who lived in a castle...

BERT: Bidirectional Encoder Representation

BERT (Bidirectional Encoder Representations from Transformers) consists of multi-layer Transformer encoders and is pre-trained using a mask mechanism.

Code example: Sentence classification using BERT

# 使用BERT进行句子分类的Python代码示例
from transformers import BertTokenizer, BertForSequenceClassification
import torch

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
labels = torch.tensor([1]).unsqueeze(0)  # 类别标签
outputs = model(**inputs, labels=labels)

loss = outputs.loss
logits = outputs.logits

print(logits)
# Output: tensor([[ 0.1595, -0.1934]])

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Language model evaluation method

Evaluating the performance of language models is a crucial task in the field of natural language processing (NLP). Different evaluation indicators and methods have a direct impact on model selection, tuning and final application scenarios. This article will introduce several commonly used evaluation methods in detail, including Perplexity, BLEU score, ROUGE score, etc., and how to implement these evaluations with code.

Perplexity

Perplexity is a common metric to measure the quality of a language model. It describes the uncertainty of the model in predicting the next word. Mathematically, perplexity is defined as the exponent of cross-entropy loss.

Code example: Calculate perplexity

import torch
import torch.nn.functional as F

# 假设我们有一个模型的输出logits和真实标签
logits = torch.tensor([[0.2, 0.4, 0.1, 0.3], [0.1, 0.5, 0.2, 0.2]])
labels = torch.tensor([1, 2])

# 计算交叉熵损失
loss = F.cross_entropy(logits, labels)

# 计算困惑度
perplexity = torch.exp(loss).item()

print(f'Cross Entropy Loss: {
      
      loss.item()}')
print(f'Perplexity: {
      
      perplexity}')
# Output: Cross Entropy Loss: 1.4068
#         Perplexity: 4.0852

BLEU score

The BLEU (Bilingual Evaluation Understudy) score is often used in machine translation and text generation tasks to measure the similarity between the generated text and the reference text.

Code example: Calculate BLEU score

from nltk.translate.bleu_score import sentence_bleu

reference = [['this', 'is', 'a', 'test'], ['this', 'is' 'test']]
candidate = ['this', 'is', 'a', 'test']
score = sentence_bleu(reference, candidate)

print(f'BLEU score: {
      
      score}')
# Output: BLEU score: 1.0

ROUGE score

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of evaluation indicators used for tasks such as automatic summarization and machine translation.

Code example: Calculate ROUGE score

from rouge import Rouge 

rouge = Rouge()

hypothesis = "the #### transcript is a written version of each day 's cnn student news program use this transcript to he    lp students with reading comprehension and vocabulary use the weekly newsquiz to test your knowledge of storie s you     saw on cnn student news"
reference = "this page includes the show transcript use the transcript to help students with reading comprehension and     vocabulary at the bottom of the page , comment for a chance to be mentioned on cnn student news . you must be a teac    her or a student age # # or older to request a chance to be mentioned on cnn student news ."

scores = rouge.get_scores(hypothesis, reference)

print(f'ROUGE scores: {
      
      scores}')
# Output: ROUGE scores: [{'rouge-1': {'f': 0.47, 'p': 0.8, 'r': 0.35}, 'rouge-2': {'f': 0.04, 'p': 0.09, 'r': 0.03}, 'rouge-l': {'f': 0.27, 'p': 0.6, 'r': 0.2}}]

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Other evaluation indicators

In addition to the perplexity, BLEU score and ROUGE score mentioned above, there are a variety of other evaluation indicators used to measure the performance of language models. These metrics may be designed for specific tasks or problems, such as text classification, named entity recognition (NER), or sentiment analysis. This section will introduce several other commonly used evaluation metrics, including precision, recall and F1 score.

Precision

Precision is a measure of how many of the samples identified as positive by the model are true positives.

Code Example: Calculate Accuracy

from sklearn.metrics import precision_score

# 真实标签和预测标签
y_true = [0, 1, 1, 1, 0, 1]
y_pred = [0, 0, 1, 1, 0, 1]

# 计算精确度
precision = precision_score(y_true, y_pred)

print(f'Precision: {
      
      precision}')
# Output: Precision: 1.0

Recall

Recall measures how many of all true positive examples were correctly identified by the model.

Code example: Calculate recall

from sklearn.metrics import recall_score

# 计算召回率
recall = recall_score(y_true, y_pred)

print(f'Recall: {
      
      recall}')
# Output: Recall: 0.8

F1 score

The F1 score is the harmonic mean of precision and recall, taking both precision and recall into consideration.

Code example: Calculate F1 score

from sklearn.metrics import f1_score

# 计算 F1 分数
f1 = f1_score(y_true, y_pred)

print(f'F1 Score: {
      
      f1}')
# Output: F1 Score: 0.888888888888889

AUC-ROC curve

AUC-ROC (Area Under the Receiver Operating Characteristic Curve) is a performance measure for binary classification problems, expressing the model's ability to classify positive and negative cases.

Code example: Calculate AUC-ROC

from sklearn.metrics import roc_auc_score

# 预测概率
y_probs = [0.1, 0.4, 0.35, 0.8]

# 计算 AUC-ROC
roc_auc = roc_auc_score(y_true, y_probs)

print(f'AUC-ROC: {
      
      roc_auc}')
# Output: AUC-ROC: 0.8333333333333333

Evaluating the performance of a language model is not limited to a single metric. Depending on application scenarios and requirements, multiple indicators may need to be combined to obtain a more comprehensive evaluation. Therefore, being familiar with and understanding these evaluation metrics is crucial to building and optimizing efficient language models.

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Summarize

Language model is a very core component in the field of natural language processing (NLP) and artificial intelligence (AI), which plays a key role in various tasks and application scenarios. With the development of deep learning technology, especially the emergence of model structures like Transformer, the capabilities of language models have been significantly improved. This progress not only promotes basic research, but also greatly promotes commercial application in industry.
Evaluating the performance of language models is a complex and multi-layered problem. On the one hand, traditional metrics like perplexity, BLEU score, and ROUGE score may not be sufficient to reflect the overall performance of the model in some scenarios. On the other hand, metrics such as precision, recall, F1-score, and AUC-ROC, while highly specific to specific tasks such as text classification, sentiment analysis, or named entity recognition (NER), are not always Suitable for all scenarios. Therefore, when evaluating language models, we should adopt multi-dimensional and multi-angle evaluation strategies, and integrate different evaluation indicators to obtain a more comprehensive and in-depth understanding.

Follow TechLead and share all-dimensional knowledge of AI. The author has more than 10 years of experience in Internet service architecture, AI product development, and team management. He is a Fudan master of Tongji, a member of Fudan Robot Intelligence Laboratory, a senior architect certified by Alibaba Cloud, a project management professional, and has hundreds of millions of revenue in AI product development. principal.