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.

1. Overview of language model

What is a language model?

Language Model (LM) is a probabilistic model used to model 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 (i.e., 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

A language model attempts to model a probability distribution ( P(w_1, w_2, \ldots, w_m) ) over a sequence of words ( 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 such a model is the normalization of the probability distribution, i.e. the sum of the probabilities of all possible word sequences must equal 1:

Challenge: high dimensionality and sparsity

Imagine that if we have a vocabulary of 10,000 words, a sentence of 20 words has (10,000^{20}) possible combinations, which is an astronomical number. Therefore, it is unrealistic to directly model this high dimensionality and sparsity.

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:

Examples

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 the language model.

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 multiple NLP applications through various strategies and optimizations, such as the chain rule and conditional probability.

2. n-gram Language Models

Basic concepts

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 most recent ( 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

# Training text, simplified version
text = "I love language models and I love coding".split()

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

# Calculate conditional probability
def bigram_probability(word1, word2):
    return bigram_freq[(word1, word2)] / unigram_freq[word1]

# output
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 space-separated words 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

Advantages

1. Simple calculation: The model parameters are easy to estimate and only need to count word frequencies.

1. Space efficiency: Compared with the full sequence model, the n-gram model needs to store much fewer parameters.

Disadvantages

1. Data sparse: For low-frequency or non-occurring n-grams, the model cannot give appropriate probability estimates.

1. Limitations: Only local (n-1 word window) word dependencies can be captured.

Summary

The n-gram language model simplifies the calculation of probability distributions through local approximation, thus solving some of the 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 can handle these challenges more effectively.

3. Neural Network Language Models

Basic concepts

Neural network language model (NNLM) attempts to use deep learning methods to solve the data sparse and limitations problems in traditional n-gram models. NNLM uses word embeddings to capture the semantic information between words and calculates the conditional probability of words through a neural network.

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

# data preparation
vocab = {"I": 0, "love": 1, "coding": 2, "<PAD>": 3} # Simplified vocabulary list
data = [0, 1, 2] # Word ID sequence of "I love coding"
data = torch.LongTensor(data)

# parameter settings
embedding_dim = 10
hidden_dim = 8
vocab_size = len(vocab)

# Define model
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

#Initialize model and optimizer
model = SimpleNNLM(vocab_size, embedding_dim, hidden_dim)
optimizer = optim.SGD(model.parameters(), lr=0.1)

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

# predict
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 “love” to be followed by “coding”.

Advantages and Disadvantages

Advantages

1. Capture long-range dependencies: Through looping or self-attention mechanisms, the model can capture longer-range dependencies.

1. Shared representation: Word embeddings can be reused in different contexts.

Disadvantages

1. Computational complexity: Compared with n-gram, NNLM has higher computational cost.

1. Data requirements: Deep models usually require large amounts of labeled data for training.

Summary

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

Training 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 to pre-train models in the field of computer vision, 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-training 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 context information of BERT and the autoregressive characteristics of GPT, and is suitable for generation tasks. Pre-training tasks include denoising autoencoders, which use a variety of ways to introduce noise on the input text.

Code example: Use PyTorch to train a simple language model

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

#Initialize the model
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)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

#Train model
for epoch in range(10):
    # Input and tags
    input_data = torch.randint(0, vocab_size, (5, 32)) # Randomly generated (sequence length, batch size) input
    target_data = torch.randint(0, vocab_size, (5, 32)) # Randomly generate labels
    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 the vocabulary index, and the output is a probability distribution predicting the likelihood of the next word.

Summary

Pretrained language models have changed 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.

Large-scale language model

In recent years, large-scale 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 in-depth explore the core principles of these models, including their structural design, pre-training tasks, and how they are used for downstream tasks. We’ll also provide code examples for a deeper understanding.

ELMo: the pioneer of dynamic word embedding

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

# Python code example for ELMo word embedding
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"

#Create model
elmo = Elmo(options_file, weight_file, 1, dropout=0)

#Convert sentences to character ids
sentences = [["I", "ate", "an", "apple"], ["I", "ate", "a", " carrot"]]
character_ids = batch_to_ids(sentences)

# Calculate embedding
embeddings = elmo(character_ids)

# Output the shape of the embedding tensor
print(embeddings['elmo_representations'][0].shape)
# Output: torch.Size([2, 4, 1024])

GPT: Generative pre-training model

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

Code example: Generating text using GPT-2

# Python code example using GPT-2 to generate text
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer

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

#Encoded text input
input_text = "Once upon a time,"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

# Generate text
with torch.no_grad():
    output = model.generate(input_ids, max_length=50)
    
# Decode the generated text
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: Using BERT for sentence classification

# Python code example using BERT for sentence classification
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) # Category labels
outputs = model(**inputs, labels=labels)

loss = outputs.loss
logits = outputs.logits

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

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 in detail several commonly used evaluation methods, including perplexity, BLEU score, ROUGE score, etc., and how to use code to implement these evaluations.

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

# Assume we have a model's output logits and true labels
logits = torch.tensor([[0.2, 0.4, 0.1, 0.3], [0.1, 0.5, 0.2, 0.2]])
labels = torch.tensor([1, 2])

# Calculate cross entropy loss
loss = F.cross_entropy(logits, labels)

# Calculate confusion
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 commonly used in machine translation and text generation tasks to measure the similarity between generated text and 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}}]

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 measures how many of the samples identified as positive by the model are truly positive.

Code example: Calculating accuracy

from sklearn.metrics import precision_score

#True labels and predicted labels
y_true = [0, 1, 1, 1, 0, 1]
y_pred = [0, 0, 1, 1, 0, 1]

# Calculation accuracy
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 rate

from sklearn.metrics import recall_score

# Calculate recall rate
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

# Calculate F1 score
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 used for binary classification problems, expressing the model’s ability to classify positive and negative examples.

Code example: Calculate AUC-ROC

from sklearn.metrics import roc_auc_score

# Predict probability
y_probs = [0.1, 0.4, 0.35, 0.8]

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

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

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

Summary

Language model is a very core component in the fields of natural language processing (NLP) and artificial intelligence (AI), which plays a key role in a variety of 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 applications 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, although metrics such as precision, recall, F1 score, and AUC-ROC are highly targeted for specific tasks such as text classification, sentiment analysis, or named entity recognition (NER), they are not always Suitable for all scenarios. Therefore, when evaluating language models, we should adopt a multi-dimensional and multi-angle evaluation strategy and combine different evaluation indicators to obtain a more comprehensive and in-depth understanding.

The article is reproduced from: techlead_krischang

Original link: https://www.cnblogs.com/xfuture/p/17828837.html