Controllable Text Generation for Large Language Models: A Survey

Controllable Text Generation for LLMs: An In-Depth Exploration

The paper "Controllable Text Generation for LLMs: A Survey" authored by Xun Liang et al. provides a rigorous examination of Controllable Text Generation (CTG) methodologies tailored for LLMs. The study systematically reviews the advancements in CTG, offering a comprehensive understanding of how LLMs can be guided to generate text under specific control conditions while maintaining high text quality standards.

Core Concepts and Task Categories

The paper defines CTG as the process by which control conditions (C) are integrated into the text generation process to produce outputs that not only exhibit desired attributes but also retain high standards of fluency, coherence, and diversity. CTG tasks are categorized into two primary types: content control (linguistic control or hard control) and attribute control (semantic control or soft control).

• Content Control focuses on managing specific elements of the generated text, such as its structure and vocabulary. This includes tasks like ensuring a specific format, controlling the organizational structure, and managing the inclusion or exclusion of specific keywords.
• Attribute Control aims to guide high-level attributes such as sentiment, style, and thematic consistency. This includes ensuring safety by avoiding toxic content, controlling sentiment orientation, and adhering to specific linguistic styles.

CTG Methods

The survey classifies CTG methods into two main stages: training-stage methods and inference-stage methods.

Training Stage Methods

1. Retraining: This involves training models from scratch using datasets with embedded control conditions or modifying existing model architectures to better align with specific requirements. Early models like CTRL introduced control codes to guide text generation.
2. Fine-Tuning: Involves making adjustments to pre-trained models using specialized datasets to embed desired control attributes. Techniques like Auxiliary Tuning and InstructCTG leverage specific datasets and instructions to refine model outputs.
3. Reinforcement Learning (RL): Utilizes reward signals to iteratively optimize the model's behavior towards specific control objectives. Approaches like SafeRLHF and GDC employ human feedback and automated reward models to balance control with content quality.

Inference Stage Methods

1. Prompt Engineering: Guides model outputs by manipulating input prompts. This includes techniques like hard prompts (explicit natural language instructions) and soft prompts (trainable vector embeddings). Methods such as Prefix-Tuning and P-Tuning fall into this category.
2. Latent Space Manipulation: Adjusts activation states within the hidden layers of the model to control text generation attributes. Techniques like Latent Steering Vectors and ICV introduce guiding vectors to achieve desired outputs without altering the model’s parameters.
3. Decoding-time Intervention: Directly manipulates the probability distribution of the generated outputs during the decoding process. This includes class-condition language model guidance methods like GeDi and DExperts, which leverage class-conditioned models to achieve precise control.

Evaluation Methods

CTG approaches are evaluated through a combination of automatic evaluation, human evaluation, and more recently, LLM-based evaluation methods.

• Automatic Evaluation: Uses metrics such as BLEU, ROUGE, and BertScore to assess text quality.
• Human Evaluation: Involves subjective assessment by human annotators, evaluating aspects like fluency, coherence, and attribute relevance.
• LLM-based Evaluation: Leverages the capabilities of advanced LLMs like ChatGPT to provide diverse and context-sensitive evaluations of the generated text.

Applications and Implications

CTG techniques have shown considerable promise across various domains, such as news generation, scientific text creation, and educational content development. These methods ensure that the generated content adheres to specific domain requirements, thereby enhancing the relevance and utility of AI-generated text in specialized fields.

In general task applications, CTG techniques address cross-domain challenges like toxicity removal, dialogue generation, and story creation, making these methods applicable across various scenarios.

Challenges and Future Directions

The paper identifies several challenges in current CTG research:

1. Reduced Fluency and Practicality: Despite advancements, issues like incoherence and semantic ambiguity persist, especially in complex tasks.
2. Complexity of Multi-Attribute Control: Controlling multiple attributes simultaneously remains a significant challenge due to the complex interdependencies among attributes.
3. Incomplete Attribute Decoupling: Spurious correlations disrupt the independence of attributes, making precise control difficult.
4. Decoding Time Optimization: The large parameter sizes of LLMs often lead to time-consuming text generation processes, affecting real-time applicability.
5. Lack of Precision in Content Control: Achieving precision in tasks requiring strict lexical control remains elusive.

The paper advocates for future research to focus on real-world applications, diversifying testing tasks, and fully leveraging the capabilities of LLMs to enhance CTG methods.

Conclusion

The survey by Liang et al. provides a comprehensive examination of CTG for LLMs, detailing various methods, evaluation techniques, and practical applications. This work identifies current challenges and suggests future research directions, offering a valuable resource for researchers aiming to advance the field of controllable text generation.