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Exploring Transformers in Natural Language Generation: GPT, BERT, and XLNet

M. Onat Topal, Anil Bas, Imke van Heerden

arXiv:2102.08036v1cs.CLcs.LG

TL;DR

Earlier NLG overviews either predated Transformers or gave them limited coverage, while recurrent models face difficulties retaining context in long sequences. The paper surveys GPT-3, BERT, and XLNet and reports model-specific capabilities and outcomes, including GPT-3’s few-shot performance and DistilBERT’s efficiency figures.

  • Problem

    Earlier NLG overviews predated Transformers or gave them limited attention, while RNNs and LSTMs have difficulty maintaining context across long sequences.

    Source: §I · p. 1, §II · p. 1

  • Method

    The paper reviews GPT-3, BERT, and XLNet, describing their Transformer-based language modeling approaches.

    Source: §VI · p. 2, §IV · p. 2

  • Results

    GPT-3 scaling improves task-agnostic few-shot performance, and DistilBERT is 40% lighter, 60% faster, and retains 97% of BERT’s language-understanding capabilities using the same data.

    Source: §III · p. 2, §IV · p. 2

  • Takeaways & Limitations

    The paper concludes that model scale affects GPT-3 performance even without task-specific fine-tuning, BERT’s bidirectionality is effective, and XLNet attempts to improve on BERT.

    Source: §VI · p. 2

Abstract

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Recent years have seen a proliferation of attention mechanisms and the rise of Transformers in Natural Language Generation (NLG). Previously, state-of-the-art NLG architectures such as RNN and LSTM ran into vanishing gradient problems; as sentences grew larger, distance between positions remained linear, and sequential computation hindered parallelization since sentences were processed word by word. Transformers usher in a new era. In this paper, we explore three major Transformer-based models, namely GPT, BERT, and XLNet, that carry significant implications for the field. NLG is a burgeoning area that is now bolstered with rapid developments in attention mechanisms. From poetry generation to summarization, text generation derives benefit as Transformer-based language models achieve groundbreaking results.

I. INTRODUCTION

The paper surveys attention mechanisms and Transformers in NLG, focusing on GPT-3, BERT, and XLNet. It addresses a coverage gap left by earlier overviews that predated Transformers or gave them limited attention.

I. INTRODUCTION

  • The paper examines GPT-3, BERT, and XLNet as examples of the rising significance of Transformers in NLG.It frames this as a response to fast-paced developments in attentional models and architectures.Source: §I · p. 1
  • Earlier NLG overviews covered developments only until 2018, before Transformers, or gave Transformers limited attention and omitted GPT-3 and XLNet.Source: §I · p. 1
  • NLG seeks to produce intelligible text, while attention has spread from NLP into areas including speech recognition and recommendation systems.Source: §I · p. 1

II. ATTENTION AND TRANSFORMERS IN NATURAL LANGUAGE GENERATION

The section contrasts the long-sequence difficulties of recurrent architectures with the Transformer’s attention-based sequence modeling. It highlights the Transformer’s potential for parallelization and longer-sequence feature capture.

II. ATTENTION AND TRANSFORMERS IN NATURAL LANGUAGE GENERATION

  • The Transformer uses attention for sequence-to-sequence modeling and eliminates recurrence and convolutions.Source: §II · p. 1
  • RNNs and LSTMs struggle to retain context across long sentences because the probability of maintaining distant-word context decreases exponentially.Source: §II · p. 1
  • Transformer parallelization enables training on larger datasets and supports capturing longer-sequence features.Source: §II · p. 1
  • Fig. 1 presents the Transformer architecture.Source: Figure 1 · p. 1

III. GPT-N

GPT-3 is presented as a scaled language model with strong task-agnostic few-shot performance, while GPT-2 illustrates learning tasks without explicit supervision. The section also describes GPT-3’s scale and examples of its generated dialogue.

III. GPT-N

  • GPT-3 is an autoregressive language model trained with 175 billion parameters to produce human-like text.Source: §III · p. 2
  • 52% was the human accuracy in identifying whether short, approximately 200-word news articles were model-generated.The paper also reports that scaling GPT-3 improves task-agnostic few-shot performance and shows question-answering and conversation examples.Source: §III · p. 2
  • Table I is identified as a set of GPT-3 dialogue examples.The prose describes examples of question-answering and natural language conversation.Source: §III · p. 2, Table I · p. 2
  • GPT-2 demonstrates that language models can learn tasks without explicit supervision, despite the typical use of task-specific supervised datasets.Source: §III · p. 2

IV. BERT-BASED MODELS

The section contrasts BERT’s bidirectional contextual representations with XLNet’s autoregressive route to bidirectional context, and describes DistilBERT’s compressed pretraining approach.

BERT

  • BERT represents words using both preceding and subsequent context, unlike context-free embeddings that assign one representation per vocabulary word.The passage describes BERT as the first deeply bidirectional, unsupervised language representation.Source: §IV · p. 2
  • BERT’s representations facilitate fine-tuning and creating NLG applications with one additional output layer.Table II presents masked-language-modeling examples using the Hugging Face implementation.Source: §IV · p. 2

DistilBERT

  • 40% lighter and 60% faster, while retaining 97% of its language understanding capabilities on the same data as original BERT, DistilBERT compresses a teacher model into a smaller student.Its training uses large batches, gradient accumulation, and dynamic masking.Source: §IV · p. 2

XLNet

  • XLNet maximizes expected likelihood to learn bidirectional contexts with an autoregressive formulation and Transformer-XL, addressing BERT’s masked-position dependency and input-corruption limitations.Source: §IV · p. 2

VI. CONCLUSION

The conclusion presents GPT-3, BERT, and XLNet as consequential Transformer-based language models and points to further areas for text-generation exploration.

VI. CONCLUSION

  • GPT-3 demonstrates the impact of Transformer language-model scale even without fine-tuning for specific tasks.Source: §VI · p. 2
  • The conclusion highlights BERT’s deep bidirectionality and effective results, while XLNet integrates Transformer-XL in an attempt to improve on BERT.Source: §VI · p. 2
  • Further Transformer-enabled text-generation exploration includes poetry, scientific writing, customer service, question answering, summarization, and virtual assistance.Source: §VI · p. 2

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