title: Controllable Text Generation for Large Language Models: A Survey
summary: In Natural Language Processing (NLP), Large Language Models (LLMs) have
  demonstrated high text generation quality. However, in real-world applications,
  LLMs must meet increasingly complex requirements. Beyond avoiding misleading or
  inappropriate content, LLMs are also expected to cater to specific user needs,
  such as imitating particular writing styles or generating text with poetic
  richness. These varied demands have driven the development of Controllable Text
  Generation (CTG) techniques, which ensure that outputs adhere to predefined
  control conditions--such as safety, sentiment, thematic consistency, and
  linguistic style--while maintaining high standards of helpfulness, fluency, and
  diversity.
    This paper systematically reviews the latest advancements in CTG for LLMs,
  offering a comprehensive definition of its core concepts and clarifying the
  requirements for control conditions and text quality. We categorize CTG tasks
  into two primary types: content control and attribute control. The key methods
  are discussed, including model retraining, fine-tuning, reinforcement learning,
  prompt engineering, latent space manipulation, and decoding-time intervention.
  We analyze each method's characteristics, advantages, and limitations,
  providing nuanced insights for achieving generation control. Additionally, we
  review CTG evaluation methods, summarize its applications across domains, and
  address key challenges in current research, including reduced fluency and
  practicality. We also propose several appeals, such as placing greater emphasis
  on real-world applications in future research. This paper aims to offer
  valuable guidance to researchers and developers in the field. Our reference
  list and Chinese version are open-sourced at
  https://github.com/IAAR-Shanghai/CTGSurvey.
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paper_authors.name: Xun Liang
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paper_authors.name: Hanyu Wang
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paper_authors._id: 66c7e5ec50ce52b61bf1ed84
paper_authors.name: Jie Hu
paper_authors._id: 66c7e5ec50ce52b61bf1ed85
paper_authors.name: Dan Liu
paper_authors._id: 66c7e5ec50ce52b61bf1ed86
paper_authors.name: Shunyu Yao
paper_authors._id: 66c7e5ec50ce52b61bf1ed87
paper_authors.name: Feiyu Xiong
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paper_authors.name: Zhiyu Li
paper_title: Controllable Text Generation for Large Language Models: A Survey
paper_summary: In Natural Language Processing (NLP), Large Language Models (LLMs) have
  demonstrated high text generation quality. However, in real-world applications,
  LLMs must meet increasingly complex requirements. Beyond avoiding misleading or
  inappropriate content, LLMs are also expected to cater to specific user needs,
  such as imitating particular writing styles or generating text with poetic
  richness. These varied demands have driven the development of Controllable Text
  Generation (CTG) techniques, which ensure that outputs adhere to predefined
  control conditions--such as safety, sentiment, thematic consistency, and
  linguistic style--while maintaining high standards of helpfulness, fluency, and
  diversity.
    This paper systematically reviews the latest advancements in CTG for LLMs,
  offering a comprehensive definition of its core concepts and clarifying the
  requirements for control conditions and text quality. We categorize CTG tasks
  into two primary types: content control and attribute control. The key methods
  are discussed, including model retraining, fine-tuning, reinforcement learning,
  prompt engineering, latent space manipulation, and decoding-time intervention.
  We analyze each method's characteristics, advantages, and limitations,
  providing nuanced insights for achieving generation control. Additionally, we
  review CTG evaluation methods, summarize its applications across domains, and
  address key challenges in current research, including reduced fluency and
  practicality. We also propose several appeals, such as placing greater emphasis
  on real-world applications in future research. This paper aims to offer
  valuable guidance to researchers and developers in the field. Our reference
  list and Chinese version are open-sourced at
  https://github.com/IAAR-Shanghai/CTGSurvey.
paper_ai_summary: Controllable Text Generation techniques for Large Language Models ensure predefined control conditions and high-quality text output, covering content and attribute control through various methods like retraining, fine-tuning, and latent manipulation.
paper_ai_keywords: Large Language Models Controllable Text Generation CTG content control attribute control model retraining fine-tuning reinforcement learning prompt engineering latent space manipulation decoding-time intervention