Latent Diffusion Energy-Based Model for Interpretable Text Modeling (2206.05895v4)

Abstract: Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling. Fueled by its flexibility in the formulation and strong modeling power of the latent space, recent works built upon it have made interesting attempts aiming at the interpretability of text modeling. However, latent space EBMs also inherit some flaws from EBMs in data space; the degenerate MCMC sampling quality in practice can lead to poor generation quality and instability in training, especially on data with complex latent structures. Inspired by the recent efforts that leverage diffusion recovery likelihood learning as a cure for the sampling issue, we introduce a novel symbiosis between the diffusion models and latent space EBMs in a variational learning framework, coined as the latent diffusion energy-based model. We develop a geometric clustering-based regularization jointly with the information bottleneck to further improve the quality of the learned latent space. Experiments on several challenging tasks demonstrate the superior performance of our model on interpretable text modeling over strong counterparts.

• The paper introduces LDEBM, a novel approach combining latent EBMs with diffusion models within a variational framework to stabilize text generation.
• The paper applies geometric clustering and the Information Bottleneck principle to enhance semantic clarity and reduce mode collapse in the latent space.
• Empirical evaluations on benchmarks like Penn Treebank and Daily Dialog demonstrate improved sampling quality and interpretability in text modeling.

Latent Diffusion Energy-Based Model for Interpretable Text Modeling: A Summary

This paper introduces the Latent Diffusion Energy-Based Model (LDEBM), a novel approach combining the latent space with diffusion models to enhance interpretability in text modeling. Energy-Based Models (EBMs), particularly in the latent space, have demonstrated potential in generative modeling due to their flexibility and power in capturing complex data distributions. However, these models inherit challenges, notably degenerate sampling quality using Markov Chain Monte Carlo (MCMC), which can hinder generation and training stability. This work proposes LDEBM to address these issues by leveraging diffusion recovery likelihood learning, aiming to improve sampling quality and model performance.

Key Contributions and Methodology

1. Symbiosis of Latent EBMs and Diffusion Models: The paper presents a symbiotic integration of Symbol-Vector Coupling EBMs and diffusion models within a variational framework. By constructing a trajectory of perturbed samples, the model effectively learns and samples sequences that capture underlying data structures while maintaining sampling quality. This integration is substantially beneficial in reducing degeneracy in sampling, as evidenced by the reduced modality in the latent space, thereby enhancing stability and reliability.
2. Geometric Clustering and Information Bottleneck (IB): To further refine the latent space, the authors propose a geometric clustering-based regularization in conjunction with the Information Bottleneck principle. The method induces clearer semantic interpretations by clustering latent variables, effectively anchoring discrete symbolic representations, and minimizing mode collapse during learning.
3. Empirical Evaluations: The empirical section thoroughly evaluates LDEBM across multiple generative and interpretive text modeling benchmarks. On the Penn Treebank dataset, LDEBM demonstrates superior performance, evidenced by competitive Reverse Perplexity (rPPL) and BLEU scores, indicating enhanced fluency and diversity of generated text. Evaluation on the Daily Dialog dataset highlights LDEBM's ability to unsupervisedly capture dialog actions and emotions, achieving high homogeneity in inferred attributes.
4. Algorithm Recapitulation: The approach involves defining a diffusion-based forward trajectory of latent variables and utilizing conditional EBMs for reverse diffusion. By optimizing an Evidence Lower Bound (ELBO) derived from both trajectories, the authors effectively coordinate inference, prior, and generation models to achieve accurate and interpretable text modeling.
5. Implications and Future Directions: The LDEBM framework not only advances the interpretability of text generation but also contributes to the ongoing exploration of improved sampling methodologies in EBMs. The approach solidifies the value of integrating diffusion models within variational settings, offering insights that could extend beyond text to other data domains.

Implications and Future Work

This paper's contributions mark a significant step forward in interpretable generative modeling by demonstrating a robust method for latent space structuring. The inclusion of diffusion processes mitigates common pitfalls of EBMs, offering a more stable and effective learning process. Future research could explore pre-trained LLMs' integration into this framework, potentially enhancing conditional generation with rich semantic understanding at a reduced computational cost. Additionally, expanding this approach to other domains such as image or audio data could present broader applications of LDEBM principles, encouraging continued exploration and refinement of EBMs in various AI fields.