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Structured Probabilistic Coding (2312.13933v5)

Published 21 Dec 2023 in cs.CL and cs.LG

Abstract: This paper presents a new supervised representation learning framework, namely structured probabilistic coding (SPC), to learn compact and informative representations from input related to the target task. SPC is an encoder-only probabilistic coding technology with a structured regularization from the target space. It can enhance the generalization ability of pre-trained LLMs for better language understanding. Specifically, our probabilistic coding simultaneously performs information encoding and task prediction in one module to more fully utilize the effective information from input data. It uses variational inference in the output space to reduce randomness and uncertainty. Besides, to better control the learning process of probabilistic representations, a structured regularization is proposed to promote uniformity across classes in the latent space. With the regularization term, SPC can preserve the Gaussian structure of the latent code and achieve better coverage of the hidden space with class uniformly. Experimental results on 12 natural language understanding tasks demonstrate that our SPC effectively improves the performance of pre-trained LLMs for classification and regression. Extensive experiments show that SPC can enhance the generalization capability, robustness to label noise, and clustering quality of output representations.

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Citations (1)
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Summary

  • The paper demonstrates that leveraging structured regularization in an encoder-only framework reduces uncertainty in latent representations.
  • It employs variational inference to maintain task-relevant information while mitigating randomness in probabilistic embeddings.
  • Empirical validation on 12 NLU tasks shows significant performance gains and improved robustness against label noise and limited data.

Introduction to Structured Probabilistic Coding

In the domain of representation learning, it's crucial to develop methods that allow models to create compact yet informative representations of input data. The new framework named Structured Probabilistic Coding (SPC) stands out by imposing structured regularization on representations to enhance the performance of pre-trained LLMs on tasks like classification and regression. The paper describes SPC as an encoder-only technology that integrates task prediction with coding, utilizing variational inference for reducing data randomness and uncertainty.

The Probabilistic Approach

Conventional methods, deterministic in nature, assign each datum a fixed vector representation. In contrast, probabilistic embedding represents each piece of data by a probability distribution. This approach manages the inherent data complexity and uncertainty more effectively. A key concept in this space is the information bottleneck principle, which influences most probabilistic embedding strategies. SPC diverges from the typical encoder-decoder architecture using variational inference to maintain and utilize the relevance of encoded task-related information.

Optimizing the Latent Space

The main critique of existing methods is the potential loss of important task-specific information during encoding, due to the inherent randomness in probability distributions. Addressing this, SPC introduces structured regularization, allowing for a constrained probabilistic distribution in the latent space that respects task-specific structures. It fosters a class-level uniformity within the latent space, which aids in achieving a distributive balance and ultimately enhances the model's predictive capabilities on new data samples.

Empirical Validation

The effectiveness of SPC is validated on a suite of 12 natural language understanding tasks, demonstrating significant performance improvements over existing methods. SPC shows robustness against label noise and generalizes well to limited data scenarios and out-of-domain settings. Such robustness to data variability is especially beneficial in real-world applications where data can often be incomplete, noisy, or biased. Furthermore, the qualitative assessment of representations' clustering quality further substantiates the SPC's capability to balance data compression and task prediction.

Summation and Acknowledgement

Structured Probabilistic Coding presents an advanced learning framework capable of improving generalization in natural language understanding tasks. The integration of structured regularization with variational inference offers a fresh perspective on managing data complexity and achieving more meaningful representations. The comprehensive experiments and ablation studies accentuate the superiority of SPC, marking it as a significant contribution to the field of supervised representation learning. The development of SPC is acknowledged to fall under the support of a national key research initiative.

SPC provides a promising direction for future research and application, pushing the frontiers of deep learning and AI more broadly into realms where data uncertainty and task specificity are managed with remarkable finesse.

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