XLNet is a transformer-based language model developed to enhance natural language understanding tasks. It introduces a generalized autoregressive pretraining method that captures bidirectional context by maximizing the expected likelihood over all permutations of the factorization order. This approach addresses limitations found in models like BERT, particularly concerning the independence assumption among masked tokens and discrepancies between pretraining and fine-tuning phases. By integrating the segment recurrence mechanism and relative positional encoding from Transformer-XL, XLNet effectively models long-range dependencies in text. Empirical evaluations demonstrate that XLNet achieves state-of-the-art performance across various benchmarks, including question answering, natural language inference, and sentiment analysis.
Key Features
Permutation-Based Language Modeling: Captures bidirectional context without relying on data corruption techniques.
Two-Stream Self-Attention Mechanism: Utilizes separate content and query streams to prevent information leakage during training.
Transformer-XL Integration: Incorporates segment-level recurrence and relative positional encoding for modeling long-term dependencies.
High-Performance Benchmarks: Outperforms previous models on tasks such as SQuAD, GLUE, and RACE.
Scalability: Designed to handle large-scale datasets efficiently, making it suitable for extensive NLP applications
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