♎
Limited AI
  • Machine Learning
    • Linear Model Cheating Sheet
    • Nonlinear Model Cheating Sheet
    • General Linear Model 1
    • General Linear Model 2
    • General Linear Model 3
    • Tree Based Methods
    • Tree Based Methods Supplement
    • XG,Cat,Light__Boosting
    • KNN&PCA
    • Model Performance
    • Model Evaluation
    • Code Practice
      • KNN
      • Decision Tree Python Code
    • Data and Feature Engineering
      • Handle Bias Data
      • Cold Start Problem
  • Deep Learning
    • Summary v2
    • Basic Neural Network
      • From Linear to Deep
      • Perceptron and Activation Function
      • NN network Details
      • Backpropagation Details
      • Gradient Vanishing vs Gradient Exploding
    • Basic CNN
      • Why CNN
      • Filter/ Convolution Kernel and Its Operation
      • Padding& Stride
      • Layers
      • Extra:From Fully Connected Layers to Convolutions
      • Extra: Multiple Input and Multiple Output Channels
    • Advance CNN
      • Convolutional Neural Networks(LeNet)
      • Deep Convolution Neural Networks(AlexNet)
      • Networks Using Blocks (VGG)
      • Network in Network(NiN)
      • Multi-Branch Networks(GoogLeNet&I mageNet)
      • Residual Networks(ResNet) and ResNeXt
      • Densely Connected Networks(DenseNet)
      • Batch Normalization
    • Basic RNN
      • Seq Model
      • Raw Text to Seq
      • Language Models
      • Recurrent Neural Networks(RNN)
      • Backpropagation Through Time
    • Advance RNN
      • Gated Recurrent Units(GRU)
      • Long Short-Term Memory(LSTM)
      • Bidirectional Recurrent Neural Networks(BRNN)
      • Encoder-Decoder Architecture
      • Seuqence to Sequence Learning(Seq2Seq)
    • Attention Mechanisms and Transformers
      • Queries, Keys, and Values
      • Attention is all you need
        • Attention and Kernel
        • Attention Scoring Functions
        • The Bahdanau Attention Mechanism
        • Multi-Head Attention
        • Self-Attention
        • Attention的实现
      • The Transformer Architecture
        • Extra Reading
        • 最短的最大路径长度
      • Large-Scaling Pretraning with Transformers
        • BERT vs OpenAI GPT vs ELMo
        • Decoder Model框架
        • Bert vs XLNet
        • T5& GPT& Bert比较
        • 编码器-解码器架构 vs GPT 模型
        • Encoder vs Decoder Reference
      • Transformers for Vision
      • Transformer for Multiomodal
    • NLP Pretraining
      • Word Embedding(word2vec)
        • Extra Reading
      • Approximate Training
      • Word Embedding with Global Vectors(GloVe)
        • Extra Reading
        • Supplement
      • Encoder(BERT)
        • BERT
        • Extra Reading
      • Decoder(GPT&XLNet&Lamma)
        • GPT
        • XLNet
          • XLNet架构
          • XLNet特点与其他比较
      • Encoder-Decoder(BART& T5)
        • BART
        • T5
  • GenAI
    • Introduction
      • GenAI Paper Must Read
      • GenAI六个阶段
    • Language Models Pre-training
      • Encoder-Decoder Architecture
      • Encoder Deep Dive
      • Decoder Deep Dive
      • Encoder VS Decoder
      • Attention Mechanism
      • Transformers
    • Example: Llama 3 8B架构
    • Fine-Tuning Generation Models
    • RAG and Adavance RAG
    • AI Agent
  • Statistics and Optimization
    • A/B testing
    • Sampling/ABtesting/GradientMethod
    • Gradient Decent Deep Dive
  • Machine Learning System Design
    • Extra Reading
    • Introduction
  • Responsible AI
    • AI Risk and Uncertainty
      • What is AI risk
      • General Intro for Uncertainty Quantification
      • Calibration
      • Conformal Prediction
        • Review the linear regression
        • Exchangeability
        • Split Conformal Prediction
        • Conformalized Quantile Regression
        • Beyond marginal coverage
        • Split Conformal Classification
        • Full Conformal Coverage
        • Cross-Validation +
        • Conformal Histgram Regression
    • xAI
      • SHAP value
  • Extra Research
    • Paper Reading
    • Reference
Powered by GitBook
On this page
  1. Deep Learning
  2. NLP Pretraining
  3. Decoder(GPT&XLNet&Lamma)

XLNet

XLNet | XLNet 模型

1. What is XLNet? | 什么是 XLNet？

XLNet is a state-of-the-art pre-trained language model introduced by researchers from Google and Carnegie Mellon University in 2019. It builds upon the Transformer-XL architecture and is designed to overcome some of the limitations of BERT and previous language models. Unlike BERT, which uses a masked language model (MLM) and is limited to bidirectional context, XLNet uses a permutation-based language modeling approach, which captures bidirectional context without masking any tokens.

XLNet outperforms previous models like BERT on various natural language processing (NLP) tasks such as text classification, question answering, and sentiment analysis, providing better generalization and improved performance. XLNet 是由谷歌和卡内基梅隆大学的研究人员在 2019 年推出的一种先进的预训练语言模型。它基于 Transformer-XL 架构，并旨在克服 BERT 和之前语言模型的一些局限性。与 BERT 使用掩蔽语言模型（MLM）且仅限于双向上下文不同，XLNet 使用 置换语言模型 方法，在不掩蔽任何标记的情况下捕捉双向上下文。

XLNet 在各种自然语言处理（NLP）任务（如文本分类、问答系统和情感分析）中优于之前的模型，如 BERT，提供了更好的泛化能力和性能提升。

2. Key Features of XLNet | XLNet 的关键特性

  • Permutation-Based Language Modeling | 基于置换的语言建模: XLNet generates all possible permutations of a sentence to predict the next token, allowing it to capture context from both the left and right, without using masking like BERT. 基于置换的语言建模：XLNet 生成句子的所有可能排列来预测下一个标记，使其能够从左右两个方向捕捉上下文，而不像 BERT 那样使用掩蔽。

  • Built on Transformer-XL | 基于 Transformer-XL: XLNet incorporates Transformer-XL, which allows the model to handle long sequences more efficiently by introducing a memory mechanism that retains past information over long distances. 基于 Transformer-XL：XLNet 引入了 Transformer-XL，通过引入记忆机制，使模型能够更有效地处理长序列，并在长距离内保留过去的信息。

  • Generalized Autoregressive Model | 广义自回归模型: Unlike traditional autoregressive models that predict tokens from left to right, XLNet uses a generalized autoregressive objective that predicts based on all possible permutations of token order. 广义自回归模型：与传统的从左到右预测标记的自回归模型不同，XLNet 使用广义自回归目标，根据标记顺序的所有可能排列进行预测。

3. How Does XLNet Work? | XLNet 是如何工作的？

Permutation-Based Language Modeling | 基于置换的语言建模

In XLNet, instead of predicting the next word based on the fixed left-to-right order or masking words as in BERT, the model generates multiple permutations of the input sequence. It then predicts each token based on its context within that specific permutation. This approach captures both the left and right context dynamically, without explicitly masking any token.

For example, for the sentence "The cat sat on the mat":

  1. XLNet generates multiple permutations such as:

    • "The sat on the cat mat"

    • "sat The on cat mat"

  2. It then predicts the next token based on the current permutation.

This permutation-based language model ensures that XLNet can model bidirectional context without the need for masking or positional dependence. 在 XLNet 中，模型不会基于固定的从左到右顺序预测下一个单词，也不会像 BERT 那样掩蔽单词，而是生成输入序列的多种排列。然后它根据每个排列中的上下文预测每个标记。这种方法动态地捕捉了左右上下文，而无需显式掩蔽任何标记。

例如，对于句子 "The cat sat on the mat"：

  1. XLNet 生成多种排列，例如：

    • "The sat on the cat mat"

    • "sat The on cat mat"

  2. 然后根据当前排列预测下一个标记。

这种 基于置换的语言建模 确保了 XLNet 可以在不需要掩蔽或位置依赖的情况下建模双向上下文。

Transformer-XL with Memory Mechanism | Transformer-XL 与记忆机制

XLNet builds on the Transformer-XL model, which introduces a memory mechanism. The memory stores information from previous sequences, allowing XLNet to process longer contexts more effectively. This improves the model’s ability to capture long-range dependencies across sequences by retaining hidden states from past segments.

In typical Transformer models, each new input sequence starts with a fresh state, which limits the model’s ability to handle long-term dependencies. Transformer-XL solves this by retaining a memory of previous hidden states and extending this memory across segments of text. XLNet 基于 Transformer-XL 模型，引入了记忆机制。该机制存储来自之前序列的信息，使 XLNet 能够更有效地处理较长的上下文。通过保留过去片段的隐藏状态，这提高了模型捕捉跨序列长程依赖关系的能力。

在典型的 Transformer 模型中，每个新的输入序列都会从一个新的状态开始，这限制了模型处理长期依赖关系的能力。而 Transformer-XL 通过保留之前隐藏状态的记忆并将其延伸到文本片段之间，解决了这一问题。

4. Advantages of XLNet | XLNet 的优势

  • Bidirectional Context Without Masking | 无需掩蔽的双向上下文: Unlike BERT, which uses masked tokens, XLNet dynamically captures both left and right context through its permutation-based modeling. 无需掩蔽的双向上下文：与使用掩蔽标记的 BERT 不同，XLNet 通过基于置换的建模动态地捕捉左右上下文。

  • Handling Long Sequences | 处理长序列的能力: With Transformer-XL's memory mechanism, XLNet can handle longer text sequences more efficiently, allowing it to capture long-range dependencies. 处理长序列的能力：借助 Transformer-XL 的记忆机制，XLNet 能够更高效地处理长文本序列，从而捕捉长程依赖关系。

  • Improved Performance on NLP Tasks | NLP 任务中的改进性能: XLNet achieves better performance on many NLP benchmarks compared to models like BERT, particularly in tasks like text classification, question answering, and natural language inference. NLP 任务中的改进性能：与 BERT 等模型相比，XLNet 在许多 NLP 基准测试中表现更好，尤其是在文本分类、问答系统和自然语言推理等任务中。

5. Limitations of XLNet | XLNet 的局限性

  • Computational Complexity | 计算复杂度: The permutation-based modeling and Transformer-XL architecture make XLNet more computationally intensive and slower to train compared to BERT. 计算复杂度：基于置换的建模和 Transformer-XL 架构使得 XLNet 的计算密集度更高，训练速度较 BERT 慢。

  • Inference Speed | 推理速度: Because of the more complex training process and permutation-based modeling, XLNet may have slower inference times compared to simpler models like GPT. 推理速度：由于更复杂的训练过程和基于置换的建模，XLNet 的推理时间可能比像 GPT 这样的简单模型更慢。

6. Applications of XLNet | XLNet 的应用

  • Text Classification | 文本分类: XLNet can classify text into categories, such as sentiment analysis, spam detection, or topic classification. 文本分类：XLNet 可以将文本分类到不同类别中，如情感分析、垃圾邮件检测或主题分类。

  • Question Answering | 问答系统: XLNet performs exceptionally well in question answering tasks by understanding both the question and the context from which the answer is derived. 问答系统：XLNet 在问答任务中表现出色，能够理解问题及其答案所来自的上下文。

  • Natural Language Inference (NLI) | 自然语言推理: XLNet can determine relationships between pairs of sentences, such as whether one sentence entails, contradicts, or is neutral to another. 自然语言推理 (NLI)：XLNet 能够确定句子对之间的关系，如一个句子是否推断、矛盾或中立于另一个句子。

  • Text Generation | 文本生成: While primarily designed for understanding, XLNet can also be used for generating coherent and contextually relevant text. 文本生成：尽管 XLNet 主要用于理解任务，它也可以用于生成连贯且上下文相关的文本。

XLNet 是一种基于 Transformer 的语言模型，通过使用基于置换的语言建模和引入 Transformer-XL 处理长序列问题，改进了 BERT。关键特性：基于置换的语言建模、带记忆的 Transformer-XL、无需掩蔽的双向上下文。优势：捕捉长程依赖，处理双向上下文，在许多 NLP 任务中性能提升。局限性：计算密集，推理速度较慢。应用：文本分类、问答系统、自然语言推理和文本生成等。

PreviousGPTNextXLNet架构

Last updated 7 months ago