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About this paper
Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, Dawei Song
This survey from the Beijing Institute of Technology and Langboat Technology systematically reviews Controllable Text Generation (CTG) techniques applied to Transformer-based Pre-trained Language Models (PLMs), categorizing current approaches, tasks, and evaluation methods. It outlines six key challenges in the field and proposes five future research directions, providing a structured understanding of this evolving area.
Key insights
- CTG methods for PLMs can be systematically classified based on their interaction with the PLM: fine-tuning, architectural retraining/refactoring, or post-processing during decoding.
- Critical challenges in PLM-based CTG include catastrophic forgetting, achieving fine-grained decoding control, ensuring global coherence, effectively incorporating external knowledge, and developing reliable evaluation metrics.
- Future advancements in CTG are likely to stem from enhanced prompt-based learning, more effective fine-grained decoding strategies, integration with classic generative theories, and novel evaluation paradigms.
Summary by alphaXiv.
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