XLNet: A Brief Introduction and Why it Outperforms BERT

Abstract: This article provides a detailed explanation of XLNet， a powerful language model that has surpassed BERT in terms of performance. XLNet introduces several key improvements over BERT， including the use of permutation-based training， a novel training objective， and the removal of the autoregressive property. This article discusses XLNet's superiority over BERT from six different aspects: pretraining objectives， context modeling， bidirectional context， attention mechanism， training data， and fine-tuning. In conclusion， XLNet's innovative design and training techniques contribute to its enhanced performance compared to BERT.

Pretraining Objectives

XLNet and BERT differ in their pretraining objectives. BERT uses masked language modeling (MLM)， where a certain percentage of tokens in a sentence are randomly masked， and the model is trained to predict the masked tokens. However， XLNet introduces a permutation-based training approach called permutation language modeling (PLM). Instead of masking tokens， XLNet randomly selects a permutation of the entire input sequence and trains the model to predict the original order of the tokens. This approach allows XLNet to capture bidirectional dependencies more effectively and prevents the model from relying solely on left-to-right or right-to-left context.

The benefits of PLM are twofold. First， it enables XLNet to model long-range dependencies better， as the model is exposed to more diverse contexts during training. Second， PLM alleviates the discrepancy between pretraining and fine-tuning， as the model learns to predict all tokens in the input sequence during pretraining， unlike BERT which only predicts the masked tokens.

Context Modeling

BERT uses the Transformer architecture with a masked self-attention mechanism to capture contextual information. However， XLNet improves upon this by introducing the Transformer-XL architecture. Transformer-XL incorporates the concept of segment-level recurrence， allowing the model to retain information from previous segments. This enables XLNet to capture longer-range dependencies and model context more effectively than BERT.

Furthermore， XLNet introduces the concept of relative positional encoding， which helps the model differentiate between tokens that have the same absolute positions but different relative positions. This is particularly useful for tasks that require understanding the relationship between tokens in a sentence.

Bidirectional Context

One of the limitations of BERT is its autoregressive property， where the model conditions on the left context to predict the next token. This restricts the model's ability to capture bidirectional context effectively. In contrast，太阳城游戏 XLNet removes the autoregressive property by training the model to predict all tokens in the sequence， regardless of their position. This enables XLNet to capture bidirectional dependencies more accurately， leading to improved performance on tasks that require a deep understanding of the context.

Attention Mechanism

XLNet introduces a novel attention mechanism called the generalized autoregressive pretraining (GPT) attention. This attention mechanism allows each token to attend to all other tokens in the sequence， including itself. By incorporating self-attention in this way， XLNet can capture more fine-grained dependencies between tokens and better model the relationships within the sequence.

Moreover， XLNet employs the Transformer-XL's memory mechanism， which extends the attention span beyond the fixed window size used in BERT. This allows the model to consider a larger context， resulting in improved language understanding.

Training Data

The training data used for XLNet pretraining plays a crucial role in its superior performance. XLNet utilizes both the masked language model (MLM) objective used by BERT and the permutation language model (PLM) objective. This combination allows XLNet to benefit from the strengths of both objectives， resulting in a more comprehensive language representation.

Additionally， XLNet employs a larger corpus for pretraining compared to BERT， which helps the model learn from a more diverse range of data. This larger training set contributes to XLNet's ability to capture a broader understanding of language.

Fine-tuning

When it comes to fine-tuning， XLNet and BERT follow similar approaches. Both models can be fine-tuned on specific downstream tasks by adding task-specific layers on top of the pretrained model. However， due to its superior pretraining objectives and context modeling， XLNet provides a stronger foundation for fine-tuning， leading to improved performance on various tasks.

Conclusion: XLNet surpasses BERT in terms of performance by introducing permutation-based training， a novel training objective， and removing the autoregressive property. XLNet's improvements in pretraining objectives， context modeling， bidirectional context， attention mechanism， training data， and fine-tuning contribute to its enhanced effectiveness. By incorporating these advancements， XLNet achieves a more comprehensive understanding of language and outperforms BERT in various natural language processing tasks.