XLNet is a type of language model that uses a technique called autoregressive modeling to predict the likelihood of a sequence of words. Unlike other language models, XLNet does not rely on a fixed order to predict the likelihood of a sequence, but instead uses all possible factorization order permutations to learn bidirectional context. This allows each position in the sequence to learn from both the left and the right, maximizing the context for each position.
What is Autoregressive Language Modeling?
Before we dive into the details of XLNet, we first need to understand what autoregressive language modeling is. Language modeling is a fundamental task in natural language processing that involves predicting the likelihood of a sequence of words. Autoregressive language modeling specifically involves predicting the likelihood of the next word in a sequence given the previous words.
For example, given the sequence "I went to the __", an autoregressive language model would predict the most likely word to follow based on the words that came before. In this case, the most likely word to follow might be "store", "beach", or "party" depending on the context.
How Does XLNet Work?
XLNet works by using autoregressive modeling to predict the likelihood of a sequence of words, but instead of using a fixed order to predict each word, it uses all possible permutations of the factorization order. This allows each position in the sequence to learn from both the left and the right, capturing bidirectional context.
For example, in a normal autoregressive language model, the word "cat" would only be able to learn from the words that came before it, like "The black and white __". However, in XLNet, the word "cat" could learn from the words that come both before and after it, like "The mouse ran away from the __, but the __ quickly caught it."
XLNet also integrates the segment recurrence mechanism and relative encoding scheme of Transformer-XL into pretraining. This improves the model's ability to handle longer text sequences, which is useful for tasks like question answering or summarization.
What are the Benefits of Using XLNet?
XLNet has several benefits over traditional autoregressive language models. By using all possible factorization orders, it is not limited by a fixed forward or backward factorization order, which can lead to better capturing of bidirectional context. Additionally, integrating the segment recurrence mechanism and relative encoding scheme of Transformer-XL into pretraining improves the model's ability to handle longer text sequences.
XLNet has achieved state-of-the-art results on several language modeling benchmarks, like the BooksCorpus and WikiText-103 datasets. It has also proven to be useful for downstream applications like question answering, machine translation, and document classification.
XLNet is a powerful autoregressive language model that leverages all possible permutations of the factorization order to capture bidirectional context. By integrating the segment recurrence mechanism and relative encoding scheme of Transformer-XL into pretraining, it is able to handle longer text sequences, making it useful for a variety of natural language processing tasks.
