XLNet: Generalized Autoregressive Pretraining for Language Understanding

XLNet (eXtreme Learning Machine) is a state-of-the-art language model developed by Google AI. It builds upon the Transformer architecture, which is a deep learning model specifically designed for sequence-to-sequence tasks such as language modeling.

Here is the original link to the paper.

Here's a breakdown of XLNet along with necessary formulas, explanations, and potential code examples:

1. Transformer Architecture: XLNet utilizes the Transformer architecture as its backbone. The key components of the Transformer architecture include:

   a. Self-Attention Mechanism: This mechanism allows the model to weigh the importance of each word/token in the context of the entire sequence. It computes attention scores between all pairs of words in a sequence and uses these scores to create context-aware representations for each word.

   b. Positional Encoding: Since Transformers do not inherently understand the order of words in a sequence, positional encodings are added to the input embeddings to provide positional information to the model.

   c. Feedforward Neural Networks: After obtaining contextualized representations through self-attention, Transformer layers typically pass the representations through feedforward neural networks to capture more complex patterns.

2. Permutation Language Modeling (PLM): XLNet introduces Permutation Language Modeling, which differs from traditional autoregressive language modeling used in models like GPT (Generative Pre-trained Transformer). In PLM, instead of conditioning on previous words sequentially, the model conditions on all permutations of the input tokens. This allows XLNet to learn bidirectional relationships between tokens.

3. Training Objective: XLNet uses a modified version of the autoregressive training objective used in models like GPT. The objective is to maximize the expected log-likelihood of the target sequence given the input sequence. Mathematically, it can be represented as:

Where:

• $(x, y)$ represents a training example consisting of an input sequence $x$ and its corresponding target sequence $y$.

• $T_y$ is the length of the target sequence.

• $\theta$ represents the model parameters.

• $P(y_t | x, y_{< t}; \theta)$ is the conditional probability of the target token $y_t$ given the input sequence $x$ and previously generated tokens $y_{< t}$.

4. Implementation: XLNet can be implemented using various deep learning frameworks such as TensorFlow or PyTorch. Below is a simplified PyTorch code snippet demonstrating how to use XLNet for text generation:

import torch
from transformers import XLNetLMHeadModel, XLNetTokenizer

# Load pre-trained XLNet model and tokenizer
model_name = 'xlnet-base-cased'
tokenizer = XLNetTokenizer.from_pretrained(model_name)
model = XLNetLMHeadModel.from_pretrained(model_name)

# Input text
input_text = "The cat sat on the"

# Tokenize input text
input_ids = tokenizer.encode(input_text, add_special_tokens=False, return_tensors="pt")

# Generate text using XLNet
max_length = 50
output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)

# Decode generated tokens
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print("Generated text:", generated_text)

This code demonstrates how to generate text using a pre-trained XLNet model. You first need to load the model and tokenizer, tokenize your input text, generate text using the model's generate method, and finally decode the generated tokens to obtain the output text.

This breakdown provides a high-level overview of XLNet, its key components, training objective, and a simple code example for text generation. For more advanced usage and fine-tuning for specific tasks, additional considerations and modifications may be required.

BERT vs XLNet

BERT (Bidirectional Encoder Representations from Transformers) and XLNet are both state-of-the-art language models, but they differ in their approach to handling bidirectionality and context modeling. Here's an explanation of how BERT lacks bidirectional context modeling and how XLNet addresses this issue:

1. BERT's Masked Language Model (MLM):

   • BERT is pre-trained using a Masked Language Model objective, where a percentage of the input tokens are randomly masked, and the model is trained to predict these masked tokens based on the surrounding context.

   • While BERT captures bidirectional context within a single training instance (i.e., it can see both left and right context during pre-training), it lacks the ability to capture bidirectional dependencies across multiple training instances. This is because each training instance (sentence or segment) is processed independently.

2. XLNet's Permutation Language Modeling (PLM):

   • XLNet, on the other hand, introduces Permutation Language Modeling, which is an improvement over BERT's approach. Instead of masking tokens as in BERT, XLNet conditions on all possible permutations of the input tokens.

   • By considering all permutations, XLNet can learn bidirectional relationships between tokens across multiple training instances. This enables XLNet to capture richer contextual information and dependencies compared to BERT.

   • Furthermore, XLNet maintains BERT's ability to capture bidirectional context within a single training instance by considering all permutations of the input tokens, including the original order.

3. Improvements by XLNet:

   • XLNet's PLM addresses the limitations of BERT by explicitly modeling bidirectional dependencies across multiple training instances.

   • XLNet achieves this by considering all possible permutations of the input tokens during training, allowing it to capture a more comprehensive understanding of the text's context.

   • As a result, XLNet tends to outperform BERT on various downstream NLP tasks that require a deeper understanding of context and dependencies across sentences or documents.

In summary, while BERT captures bidirectional context within a single training instance through masked language modeling, it lacks the ability to model bidirectional dependencies across multiple instances. XLNet addresses this limitation by introducing Permutation Language Modeling, which enables it to capture bidirectional relationships across multiple instances, leading to improved performance on various NLP tasks.

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