Controllable Text Generation for Large Language Models: A Survey
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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Zhiyu Li
Controllable Text Generation for Large Language Models: A Survey
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.
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.
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