Controllable Text Generation for Large Language Models: A Survey
Abstract
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.
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.
Community
Hello everyone,
Iโm 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
Click to expand content
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. Weโve also made all references and a Chinese version of the survey available on GitHub.
We would greatly appreciate your supportโplease give us a like or share on GitHub and arXiv, and feel free to reach out with any feedback or collaboration opportunities!
ๅคงๅฎถๅฅฝ๏ผ
ๆไปฌๅพ้ซๅ ดไธๅคงๅฎถๅไบซๆไปฌ็ๆๆฐ็ปผ่ฟฐ่ฎบๆ๏ผใControllable Text Generation for Large Language Models: A Surveyใใๆฌ็ปผ่ฟฐๆทฑๅ ฅๆข่ฎจไบๅฏๆงๆๆฌ็ๆ๏ผCTG๏ผ็ๅๆฒฟ้ขๅ๏ผๅๆไบ่ต่ฝๅคง่งๆจก่ฏญ่จๆจกๅ๏ผLLMs๏ผ็ๆๆด็ฒพๅๅๅฎๅถๅๆๆฌ็ๅค็งๆๆฏไธๆนๆณใ
ๅฎๆด็็ปผ่ฟฐๅ็ธๅ ณ่ตๆบๅฏไปฅ้่ฟไปฅไธ้พๆฅ่ฎฟ้ฎ๏ผ
- GitHub ไปๅบ: https://github.com/IAAR-Shanghai/CTGSurvey
- arXiv ่ฎบๆ: https://arxiv.org/abs/2408.12599
- PDF ็ๆฌ: https://arxiv.org/pdf/2408.12599
็นๅปๅฑๅผ่ฏฆ็ปๅ ๅฎน
ๅฏๆงๆๆฌ็ๆ็้่ฆๆง
้็ๅฏนLLMs็ๆ็ฌฆๅ็นๅฎ้ๆฑๆๆฌ็ๅ ด่ถฃๅ้ๆฑๆฅ็ๅข้ฟ๏ผCTG็ ็ฉถๆญฃๅจ่ฟ ้ๅๅฑใCTG่ฝๅค็กฎไฟ็ๆ็ๆๆฌ็ฌฆๅ้ข่ฎพ็ๆงๅถๆกไปถ๏ผๅฆๅฎๅ จๆงๆๆ ๆ๏ผ๏ผๅๆถไฟๆๆต็ ๆงๅๅคๆ ทๆง็้ซ่ดจ้่พๅบใCTGๅ ทๅคไธคไธชๅ ณ้ฎ้ๆฑ๏ผ
็ฌฆๅ้ขๅฎ็ๆงๅถๆกไปถ๏ผ ็กฎไฟ็ๆ็ๆๆฌๆปก่ถณ็นๅฎๆ ๅ๏ผๅฆไธป้ข็ธๅ ณๆงใๅฎๅ จ่ฆๆฑๅ้ฃๆ ผไธ่ดๆงใ
ไฟๆ้ซ่ดจ้่พๅบ๏ผ ๅจๆงๅถๆๆฌ็ๆ็ๅๆถ๏ผ็กฎไฟ็ๆ็ๅ ๅฎนๆต็ ใ่ฟ่ดฏไธๅคๆ ทๅ๏ผไฝฟๆๆฌๅ ทๆๅธๅผๅๅๅฎ็จๆงใ
ๆไปฌๅฆไฝๅฎไนๅฏๆงๆๆฌ็ๆ
ๆไปฌๅฐCTGๅฎไนไธบLLMs็ไธ้กนๅ ณ้ฎ่ฝๅ๏ผๅ ถๆ ธๅฟๅจไบๆ นๆฎ็นๅฎ้ๆฑ๏ผๅฆ้ฃๆ ผใๆ ๆๆๅฎๅ จๆง๏ผ็ๆ็ฌฆๅ่ฆๆฑ็ๆๆฌใๆงๅถๆกไปถๅฏไปฅๅจๆๆฌ็ๆ็ๅไธช้ถๆฎต่ฟ่กๆดๅ๏ผ็กฎไฟ็ๆ็ๆๆฌๆข็ฌฆๅๆ ๅๅไฟๆ้ซ่ดจ้ใ
ๆไปฌ็ปผ่ฟฐ็้็น้ขๅ
CTGไปปๅกๅ็ฑป๏ผ
- ๅ ๅฎนๆงๅถ๏ผ็กฌๆงๅถ๏ผ๏ผ ็ฎก็ๆๆฌๅ ๅฎน็็ปๆๅๆ ผๅผ๏ผๅ ๆฌ่ฏๆฑ้ๆฉๅ็ป็ปๆนๅผใ
- ๅฑๆงๆงๅถ๏ผ่ฝฏๆงๅถ๏ผ๏ผ ็ฎก็ๆๆฌ็ๆ ๆใ้ฃๆ ผๅๅฎๅ จๆง็ญๅฑๆง๏ผ็กฎไฟ็ๆ็ๆๆฌ็ฌฆๅ็นๅฎ็ฎๆ ใ
CTGๆนๆณๅญฆ๏ผ
- ่ฎญ็ป้ถๆฎตๆนๆณ๏ผ ๅ ๆฌๆจกๅๅ่ฎญ็ปใๅพฎ่ฐๅๅผบๅๅญฆไน ็ญๆๆฏ๏ผ้่ฟๅตๅ ฅๆงๅถๆกไปถๅฝฑๅๆจกๅ็ๆ็ๆๆฌใ
- ๆจ็้ถๆฎตๆนๆณ๏ผ ๅฆๆ็คบๅทฅ็จใๆฝๅจ็ฉบ้ดๆๆงๅ่งฃ็ ่ฟ็จไธญ็ๅนฒ้ขๆๆฏ๏ผๅจๆจ็้ถๆฎตๅฝฑๅ่พๅบๅ ๅฎนใ
่ฏไผฐไธๅบ็จ๏ผ
- ๆไปฌๅ้กพไบๅ็ง่ฏไผฐๆนๆณ๏ผๅ ๆฌ่ชๅจๅ่ฏไผฐๆๆ ๅไบบๅทฅ่ฏไผฐ๏ผไปฅ่กก้CTGๆๆฏ็ๆๆๆงใ
- CTG็ๅบ็จ่ๅดๅนฟๆณ๏ผๆถต็ไบๅคไธชไธไธ้ขๅๅ้็จไปปๅก๏ผๅฑ็คบไบๅ ถๅคๆ ทๆงๅ้่ฆๆงใ
ๆๆไธๆชๆฅๆนๅ
ๆไปฌ็็ปผ่ฟฐ่ฟๆข่ฎจไบๅจๅฎ็ฐ็ฒพ็กฎๆงๅถ็ๅๆถไฟๆๆๆฌ่ดจ้ๆ้ขไธด็ๆๆ๏ผๅนถๆๅบไบๆจๅจCTG็ ็ฉถ็ๆชๆฅๆนๅใ
่ฟ็ฏ็ปผ่ฟฐๆจๅจไธบไปไบๆๅฏนๅฏๆงๆๆฌ็ๆๆๅ ด่ถฃ็็ ็ฉถไบบๅๆไพๆไปทๅผ็ๅ่่ตๆบใๆไปฌๅจGitHubไธๅผๆพไบๆๆ่ฎบๆๅ่่ตๆ๏ผๅนถๆไพไบ่ฏฅ็ปผ่ฟฐ็ไธญๆ็ใ
้ๅธธๆ่ฐขๆจ็ๆฏๆโโ่ฏทๅจGitHubๅarXivไธไธบๆไปฌ็ๅทฅไฝ็น่ตๆๅไบซ๏ผๅนถ้ๆถไธๆไปฌ่็ณป๏ผๆไพๅ้ฆๆๅไฝๅปบ่ฎฎ๏ผ
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