Controllable Text Generation for Large Language Models: A Survey

Original Paper: https://arxiv.org/abs/2408.12599

By: Xun Liang, Hanyu Wang, Yezhaohui Wang, Shichao Song, Jiawei Yang, Simin Niu, Jie Hu, Dan Liu, Shunyu Yao, Feiyu Xiong, Zhiyu Li

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.

Summary Notes

Figure. Survey Framework

Figure. Classification of Controllable Text Generation Methods

In the rapidly evolving world of Natural Language Processing (NLP), Large Language Models (LLMs) have made significant strides in generating high-quality text. However, as the demand for more sophisticated and precise text generation grows, the need for Controllable Text Generation (CTG) has become increasingly apparent. This blog post will delve into the latest research on CTG, exploring methodologies, key findings, and potential applications that promise to revolutionize how we harness LLMs.

Introduction: Why Controllable Text Generation Matters

LLMs like GPT-3 have demonstrated remarkable capabilities in generating coherent and contextually relevant text. Yet, in real-world applications, these models must meet more complex requirements beyond mere fluency.

From avoiding content harmful in customer service interactions to maintaining thematic consistency in news reporting, CTG techniques are crucial for ensuring that generated text adheres to specific control conditions while retaining high standards of quality.

Key Methodologies in Controllable Text Generation

The latest research on CTG categorizes methodologies into two main stages: training and inference. Each stage encompasses various techniques designed to inject control conditions into the text generation process.

Training Stage Methods

1. Retraining:
   • Approach: Involves training models from scratch or making significant adjustments to existing models.
   • Example: The Conditional Transformer Language Model (CTRL) uses control codes to guide text generation according to desired attributes like domain or style.
   • Advantage: Provides comprehensive control but requires extensive computational resources.
2. Fine-Tuning:
   • Approach: Adjusts pre-trained models using smaller, task-specific datasets.
   • Example: FLAN (Finetuned LAnguage Net) uses instruction tuning to enhance zero-shot task performance.
   • Advantage: Balances performance and resource efficiency, making it a popular choice.
3. Reinforcement Learning:
   • Approach: Utilizes feedback signals to iteratively optimize model outputs.
   • Example: Reinforcement Learning from Human Feedback (RLHF) uses human comparisons to fine-tune model summarization strategies.
   • Advantage: Effective for complex tasks but involves long training cycles.

Inference Stage Methods

1. Prompt Engineering:
   • Approach: Uses specific input prompts to guide model output without changing model parameters.
   • Example: Prefix-Tuning optimizes continuous vectors (prefixes) to guide text generation.
   • Advantage: Flexible and resource-efficient, suitable for quick adjustments.
2. Latent Space Manipulation:
   • Approach: Adjusts activation states within the model’s hidden layers to control text attributes.
   • Example: GENhance trains an encoder to separate latent vectors related to CTG target attributes.
   • Advantage: Allows precise control, especially for multi-attribute tasks.
3. Decoding-time Intervention:
   • Approach: Modifies the probability distribution of the generated output during decoding.
   • Example: Plug and Play Language Model (PPLM) adjusts hidden layer activations using gradients from an attribute classifier.
   • Advantage: Enables dynamic adjustments but can impact text fluency.

Findings and Results

Recent studies have highlighted several key insights:

• Attribute Control: Methods like CTRL and CoCon demonstrate the potential to control high-level attributes such as sentiment and theme. Innovations in fine-grained control (e.g., Director model) enhance precision but may increase computational complexity.
• Content Control: Techniques like POINTER and CBART effectively manage specific text content, such as keyword inclusion. However, they often require significant architectural modifications.
• Trade-offs: While training-phase methods offer robust control, they demand substantial resources. Inference-phase methods provide flexibility but may compromise fluency and consistency.

Implications and Applications

The implications of advancing CTG are far-reaching. Here are some potential applications:

• Customer Service: Ensuring responses are non-toxic and maintain a positive tone can enhance customer experience.
• News Reporting: Generating accurate and contextually relevant news articles while avoiding misleading content.
• Education: Customizing learning materials to different reading levels by controlling lexical complexity.

Conclusion: The Road Ahead

The field of Controllable Text Generation is poised for significant advancements. Future research should focus on real-world applications, expanding the diversity of testing tasks, and maximizing the capabilities of LLMs.

By addressing challenges like reduced fluency, multi-attribute control, and decoding time optimization, we can unlock the full potential of CTG, making AI-generated text more reliable, relevant, and useful across various domains.