Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (2004.04092v4)

Abstract: When trained effectively, the Variational Autoencoder (VAE) can be both a powerful generative model and an effective representation learning framework for natural language. In this paper, we propose the first large-scale language VAE model, Optimus. A universal latent embedding space for sentences is first pre-trained on large text corpus, and then fine-tuned for various language generation and understanding tasks. Compared with GPT-2, Optimus enables guided language generation from an abstract level using the latent vectors. Compared with BERT, Optimus can generalize better on low-resource language understanding tasks due to the smooth latent space structure. Extensive experimental results on a wide range of language tasks demonstrate the effectiveness of Optimus. It achieves new state-of-the-art on VAE LLMing benchmarks. We hope that our first pre-trained big VAE LLM itself and results can help the NLP community renew the interests of deep generative models in the era of large-scale pre-training, and make these principled methods more practical.

Overview of "Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space"

The paper outlines "Optimus," the first large-scale Variational Autoencoder (VAE) model crafted to manage sentence representations within a universal latent space. This innovative model fuses the generative prowess of VAEs with pre-trained LLMs (PLMs) to enhance both language generation and understanding tasks. Leveraging extensive pre-training on large text corpora, Optimus constructs a structured latent semantic space, allowing effective adaptation across various NLP applications.

Key Contributions and Findings

• Unified Framework: Optimus bridges the strengths of BERT and GPT-2 within a VAE architecture. While both BERT and GPT-2 serve foundational roles in language understanding and generation, Optimus uniquely supports controlled generation by encapsulating sentences in a smooth latent space. This latent space is instrumental in providing semantic organization, differentiating Optimus from preceding PLMs.
• LLMing Performance: Optimus sets new benchmarks in VAE LLMing tasks by outperforming small VAEs and state-of-the-art models like GPT-2 on critical metrics including perplexity, Mutual Information (MI), and Active Units (AU). The proposed architecture largely mitigates the KL vanishing issue, thereby enhancing model capacity and empirical performance.
• Guided Language Generation: The model's ability to pre-train a universal latent space facilitates highly controlled and guided language generation, surpassing the capabilities of traditional NLMs and GPT-2, which lack structured semantic manipulation.
• Low-Resource Scenarios: Demonstrating superior generalization, Optimus shows improved adaptability over BERT in low-resource language understanding tasks. The smooth latent space learned by Optimus proves beneficial, notably when datasets are limited.

Methodological Innovations

1. Latent Vector Injection: The paper illustrates effective methodologies for injecting latent vectors into GPT-2's architecture, enhancing guided text generation without necessitating complete retraining.
2. Fusion of BERT/GPT-2 with VAE: This integration serves as a practical guide for leveraging existing PLMs in building larger, modular models.
3. Pre-training Effectiveness: Pre-training over massive datasets inherently reduces KL vanishing, a crucial enhancement for latent space modeling.
4. Information-Theoretic Insights: The paper extends the understanding of VAEs from an information bottleneck perspective, elucidating how VAEs strike a balance between compactness and usability of learned representations.

Implications and Future Work

The research illustrates promising implications for the evolution of AI in NLP. Optimus not only enhances existing generative language capabilities but also paves the way for future models to incorporate deep generative insights within PLM frameworks. Its adaptability in low-resource settings and versatility across NLP tasks suggest potential applications in personalized and contextual LLMs.

Future directions could explore further scalability of latent spaces, more sophisticated mechanisms for controlling extended text, and the comprehensive potential of integrating PLMs with various generative models to broaden the NLP horizon. The release of code and models supports the continuous advancement and adoption of latent-variable solutions in commercial and academic domains.