Diffusion-LM Improves Controllable Text Generation (2205.14217v1)

Abstract: Controlling the behavior of LLMs (LMs) without re-training is a major open problem in natural language generation. While recent works have demonstrated successes on controlling simple sentence attributes (e.g., sentiment), there has been little progress on complex, fine-grained controls (e.g., syntactic structure). To address this challenge, we develop a new non-autoregressive LLM based on continuous diffusions that we call Diffusion-LM. Building upon the recent successes of diffusion models in continuous domains, Diffusion-LM iteratively denoises a sequence of Gaussian vectors into word vectors, yielding a sequence of intermediate latent variables. The continuous, hierarchical nature of these intermediate variables enables a simple gradient-based algorithm to perform complex, controllable generation tasks. We demonstrate successful control of Diffusion-LM for six challenging fine-grained control tasks, significantly outperforming prior work.

Analyzing "Diffusion-LM Improves Controllable Text Generation"

The paper presents Diffusion-LM, a novel framework for non-autoregressive LLMing with a focus on enabling controllable text generation. This approach builds upon the recent advancements in diffusion models applied to continuous domains like image and audio, adapting the method to handle the inherently discrete domain of text.

Key Contributions

1. Non-Autoregressive Model: Diffusion-LM deviates from the typical autoregressive structure, allowing for continuous denoising of Gaussian vectors into word vectors. This is achieved by iteratively mapping noisy vectors to latent variables, facilitating complex and fine-grained control tasks in text generation.
2. Gradient-Based Control: The model employs a straightforward gradient-based algorithm to apply fine-grained constraints during text generation. This makes it possible to steer the generation process effectively without needing to retrain the model for each new task.
3. Extensive Control Tasks: The efficacy of Diffusion-LM is demonstrated across six challenging control tasks, showing significant performance improvements over existing methods. These tasks range from semantic and syntactic constraints to more nuanced controls like parse trees and sentence infilling.

Numerical Results

The results indicate that Diffusion-LM nearly doubles the success rate of previous plug-and-play methods and rivals fine-tuning approaches that require extensive retraining. For instance, the model demonstrated improved success rates in semantic content and syntactic structure tasks, surpassing conventional methods in both control accuracy and linguistic fluency.

Implications

Diffusion-LM opens new directions in text generation research by showcasing the potential of non-autoregressive models for complex, controllable generation tasks.

• Practical Applications: Its ability to maintain fluency while adhering to complex constraints can enhance applications in supervised learning tasks where fine control over output structure is necessary.
• Theoretical Insights: The use of continuous diffusion for discrete text data introduces a novel perspective that challenges traditional autoregressive methodologies. It emphasizes the utility of latent space representations and continuous optimizations even in discrete domains.

Future Developments

Several areas present opportunities for further exploration and refinement:

• Scalability: Optimizing the diffusion process to reduce computational overhead and improve training efficiency could make this approach more applicable to large-scale text modeling tasks.
• Robustness: Enhancing the model's robustness to varied noise schedules and embeddings could improve its adaptability across differing text datasets and languages.
• Integration with Other Models: Exploring hybrid models that integrate autoregressive and diffusion-based elements might capture the strengths of both paradigms, offering balanced trade-offs between controllability and computational cost.

In conclusion, Diffusion-LM introduces a promising framework that transcends traditional limitations in controllable text generation. By effectively leveraging continuous diffusion processes, it achieves significant advances in generating nuanced and contextually accurate text, thus paving the way for more sophisticated LLMing techniques.