arxiv:2408.12599

Controllable Text Generation for Large Language Models: A Survey

Published on Aug 22

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UglyToilet on Aug 23

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Authors:

Hanyu Wang ,

Yezhaohui Wang ,

Shichao Song ,

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.

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UglyToilet

Paper author Paper submitter 24 days ago

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edited 24 days ago

Hello everyone,

I’m excited to share our latest survey paper, "Controllable Text Generation for Large Language Models: A Survey." This comprehensive work delves into the field of Controllable Text Generation (CTG), offering an in-depth analysis of the techniques and methodologies that enable more precise and tailored text generation in large language models (LLMs).

Explore the full survey and related resources:

GitHub Repository: https://github.com/IAAR-Shanghai/CTGSurvey
arXiv Paper: https://arxiv.org/abs/2408.12599
PDF Version: https://arxiv.org/pdf/2408.12599

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Why is Controllable Text Generation Important?

As interest in enabling LLMs to generate content that meets specific requirements grows, CTG research is expanding rapidly. CTG ensures that generated text adheres to predefined control conditions, like safety or sentiment, while maintaining quality in fluency and diversity. CTG addresses two key needs:

Adherence to Predefined Control Conditions: Ensuring that generated text meets specific criteria, such as thematic relevance, safety standards, and stylistic consistency.
Maintaining High-Quality Output: Balancing control with the need for fluent, coherent, and diverse text, which remains engaging and useful.

How We Define Controllable Text Generation

We define CTG as a capability of LLMs that focuses on presenting information to meet specific needs, such as style, sentiment, or safety. Control conditions can be integrated at various stages, ensuring that generated text aligns with predefined criteria while maintaining overall quality.

Key Areas of Focus in Our Survey

Classification of CTG Tasks:
- Content Control (Hard Control): Managing the structure and format of the content, including vocabulary and organization.
- Attribute Control (Soft Control): Managing attributes like sentiment, style, and safety to ensure the generated text meets specific goals.
CTG Methodologies:
- Training-Stage Methods: Techniques like model retraining, fine-tuning, and reinforcement learning that embed control conditions during training.
- Inference-Stage Methods: Techniques like prompt engineering, latent space manipulation, and decoding-time interventions that influence the output during inference.
Evaluation and Applications:
- We review various evaluation methods, including both automatic metrics and human assessments, to measure the effectiveness of CTG techniques.
- CTG applications span specialized domains and general tasks, highlighting its versatility and importance.

Challenges and Future Directions

Our survey also addresses the challenges researchers face in achieving precise control while maintaining text quality, and suggests future directions for advancing CTG research. We emphasize the need for robust evaluation frameworks and the application of CTG techniques in real-world scenarios.

This paper aims to be a valuable resource for anyone working in or interested in Controllable Text Generation. We’ve also made all references and a Chinese version of the survey available on GitHub.

We would greatly appreciate your support—please give us a like or share on GitHub and arXiv, and feel free to reach out with any feedback or collaboration opportunities!

大家好，

我们很高兴与大家分享我们的最新综述论文，《Controllable Text Generation for Large Language Models: A Survey》。本综述深入探讨了可控文本生成（CTG）的前沿领域，分析了赋能大规模语言模型（LLMs）生成更精准和定制化文本的多种技术与方法。

完整的综述和相关资源可以通过以下链接访问：

GitHub 仓库: https://github.com/IAAR-Shanghai/CTGSurvey
arXiv 论文: https://arxiv.org/abs/2408.12599
PDF 版本: https://arxiv.org/pdf/2408.12599

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可控文本生成的重要性

随着对LLMs生成符合特定需求文本的兴趣和需求日益增长，CTG研究正在迅速发展。CTG能够确保生成的文本符合预设的控制条件（如安全性或情感），同时保持流畅性和多样性的高质量输出。CTG具备两个关键需求：

符合预定的控制条件： 确保生成的文本满足特定标准，如主题相关性、安全要求和风格一致性。
保持高质量输出： 在控制文本生成的同时，确保生成的内容流畅、连贯且多样化，使文本具有吸引力和实用性。

我们如何定义可控文本生成

我们将CTG定义为LLMs的一项关键能力，其核心在于根据特定需求（如风格、情感或安全性）生成符合要求的文本。控制条件可以在文本生成的各个阶段进行整合，确保生成的文本既符合标准又保持高质量。

我们综述的重点领域

CTG任务分类：
- 内容控制（硬控制）： 管理文本内容的结构和格式，包括词汇选择和组织方式。
- 属性控制（软控制）： 管理文本的情感、风格和安全性等属性，确保生成的文本符合特定目标。
CTG方法学：
- 训练阶段方法： 包括模型再训练、微调和强化学习等技术，通过嵌入控制条件影响模型生成的文本。
- 推理阶段方法： 如提示工程、潜在空间操控和解码过程中的干预技术，在推理阶段影响输出内容。
评估与应用：
- 我们回顾了各种评估方法，包括自动化评估指标和人工评估，以衡量CTG技术的有效性。
- CTG的应用范围广泛，涵盖了多个专业领域和通用任务，展示了其多样性和重要性。