Key Points
1. The paper systematically reviews the latest advancements in Controllable Text Generation (CTG) for Large Language Models (LLMs), offering a comprehensive definition of its core concepts and clarifying the requirements for control conditions and text quality.
2. It categorizes CTG tasks into two primary types: content control (or linguistic control/hard control) and attribute control (or semantic control/soft control).
3. The key CTG methods discussed include model retraining, fine-tuning, reinforcement learning, prompt engineering, latent space manipulation, and decoding-time intervention. The paper analyzes the characteristics, advantages, and limitations of each method.
4. The paper reviews CTG evaluation methods, summarizing its applications across domains, and addresses key challenges in current research, including reduced fluency, complexity of multi-attribute control, incomplete attribute decoupling, decoding time optimization, and lack of precision in content control.
5. The paper advocates for diversifying test tasks, emphasizing practical applications, and maximizing the capabilities of LLMs when comparing baselines.
6. Compared to existing surveys, the core contributions of this review include a focus on Transformer architecture, emphasis on Large Language Models, exploration of model expression and CTG quality, an innovative task classification framework, and a systematic classification of CTG methods.
7. The paper defines Controllable Text Generation within the context of LLMs, explaining how control conditions are integrated into the generation process, and introduces the concept of the semantic space representation of CTG.
8. The training stage methods for CTG include retraining, fine-tuning, and reinforcement learning, each with their own advantages and limitations.
9. The inference stage methods provide flexible and dynamic text control capabilities, including prompt engineering, latent space manipulation, decoding-time intervention, and various guidance techniques, each with their own strengths and challenges.
Summary
Advances in Large Language Models
The rapid advancement of Large Language Models (LLMs) has enabled significant improvements in text generation quality. However, real-world applications now demand that LLMs meet increasingly complex requirements beyond just avoiding inappropriate content. LLMs are expected to cater to specific user needs, such as imitating particular writing styles or generating text with poetic richness.
Development of Controllable Text Generation
These varied demands have driven the development of Controllable Text Generation (CTG) techniques. CTG aims to ensure that LLM outputs adhere to predefined control conditions while maintaining high standards of helpfulness, fluency, and diversity. Control conditions in CTG can be explicit, like clear instructions through prompts, or implicit, ensuring outputs meet certain standards even when not explicitly stated. CTG can be viewed as an ability dimension orthogonal to an LLM's objective knowledge capabilities. While LLMs excel at logical reasoning, text analysis and problem-solving, CTG emphasizes how this information is expressed and presented. For example, in sentiment control, the focus is on conveying the desired emotional tone, rather than just the factual accuracy.
Categories of CTG Tasks
The authors categorize CTG tasks into two primary types: content control (hard control) and attribute control (soft control). Content control focuses on specific text elements like structure and vocabulary, while attribute control targets abstract language attributes like sentiment, style and topic.
Key CTG Methods
Key CTG methods are discussed, including retraining, fine-tuning, reinforcement learning, prompt engineering, latent space manipulation, and decoding-time intervention. Each method's characteristics, advantages and limitations are analyzed, providing insights for achieving generation control.
Evaluation and Application of CTG
The paper also reviews CTG evaluation methods, summarizes applications across domains, and addresses challenges like reduced fluency, complexity of multi-attribute control, and achieving precise content control. The authors propose appeals to place greater emphasis on practical real-world applications in future research. Overall, this comprehensive survey offers valuable guidance to researchers and developers in the field of controllable text generation for large language models.
Reference: https://arxiv.org/abs/2408.12599