Autoregressive Language Model

An Autoregressive Language Model is a language model that is a next-token prediction model (autoregressive model).

• Example(s):
  • a Unidirectional One-Token-at-a-Time LLM, such as a GPT Model, BLOOM Model.
  • …
• Counter-Example(s):
  • Bidirectional Text Model, such as BERT model.
  • …
• See: XLNet, Text-to-Text Model.

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References

2019

• (Yang, Dai et al., 2019) ⇒ Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R. Salakhutdinov, and Quoc V. Le. (2019). “Xlnet: Generalized Autoregressive Pretraining for Language Understanding.” Advances in Neural Information Processing Systems, 32.
  • ABSTRACT: With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment setting, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking.

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