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A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models

Created by
  • Haebom

Author

Lingzhe Zhang, Liancheng Fang, Chiming Duan, Minghua He, Leyi Pan, Pei Xiao, Shiyu Huang, Yunpeng Zhai, Xuming Hu, Philip S. Yu, Aiwei Liu

Outline

This paper presents a systematic investigation of parallel text generation methods for large-scale language models (LLMs). Conventional autoregressive (AR) text generation suffers from the limitation of slow speed due to its sequential token-by-token generation. To overcome this limitation, parallel text generation methods have emerged. This paper categorizes AR-based and non-AR-based parallel text generation methods and analyzes their theoretical advantages and disadvantages in terms of speed, quality, and efficiency. Furthermore, we examine the combinatorial potential of various methods and compare them with other acceleration strategies. We present recent developments, unresolved challenges, and future research directions. We also publish a GitHub repository containing related papers and materials.

Takeaways, Limitations

Takeaways:
Provides a systematic classification and analysis of parallel text generation methods to enhance the understanding of related research.
Comparative analysis of the pros and cons of various parallel text generation methods in terms of speed, quality, and efficiency to help select the optimal method.
Contribute to the development of parallel text generation technology by suggesting future research directions.
Improve research accessibility by providing a GitHub repository that organizes relevant materials.
Limitations:
The classification scheme presented in this paper may not comprehensively cover all parallel text generation methods.
Since this is based on theoretical analysis and not actual implementation and performance evaluation results, there may be differences from actual performance.
Although it reflects the latest research trends, new methodologies may emerge after the paper is published.
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