Controllable Text Generation for Large Language Models: A Survey
Xun Liang, Hanyu Wang, Yezhaohui Wang, Shichao Song, Jiawei Yang, Simin Niu, Jie Hu, Dan Liu, Shunyu Yao, Feiyu Xiong, Zhiyu Li
2024-08-23
Summary
This paper provides an overview of controllable text generation techniques for large language models (LLMs), focusing on how to generate text that meets specific user needs and quality standards.
What's the problem?
Large language models are great at generating text, but they often struggle to produce content that fits specific requirements, such as avoiding inappropriate language or mimicking a certain writing style. This can be a problem in real-world applications where users have particular expectations.
What's the solution?
The authors review various methods for controllable text generation, which allow models to produce text that aligns with predefined guidelines. They categorize these methods into two types: content control (what the text is about) and attribute control (how the text is presented). Techniques discussed include retraining models, fine-tuning, and using reinforcement learning. The paper also highlights the challenges in maintaining text quality while ensuring it meets control requirements.
Why it matters?
This research is important because it helps improve how AI systems generate text, making them more useful for applications like customer service, content creation, and educational tools. By focusing on controllability, developers can create more reliable and tailored AI solutions that better serve user needs.
Abstract
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.