Reconsidering Degeneration of Token Embeddings with Definitions for Encoder-based Pre-trained Language Models

Abstract: Learning token embeddings based on token co-occurrence statistics has proven effective for both pre-training and fine-tuning in natural language processing. However, recent studies have pointed out that the distribution of learned embeddings degenerates into anisotropy (i.e., non-uniform distribution), and even pre-trained LLMs (PLMs) suffer from a loss of semantics-related information in embeddings for low-frequency tokens. This study first analyzes the fine-tuning dynamics of encoder-based PLMs and demonstrates their robustness against degeneration. On the basis of this analysis, we propose DefinitionEMB, a method that utilizes definitions to re-construct isotropically distributed and semantics-related token embeddings for encoder-based PLMs while maintaining original robustness during fine-tuning. Our experiments demonstrate the effectiveness of leveraging definitions from Wiktionary to re-construct such embeddings for two encoder-based PLMs: RoBERTa-base and BART-large. Furthermore, the re-constructed embeddings for low-frequency tokens improve the performance of these models across various GLUE and four text summarization datasets.

• The paper analyzes token embedding degeneration (anisotropy) in pre-trained language models (PLMs) and introduces DefinitionEMB to create semantically rich, isotropically distributed embeddings using Wiktionary definitions.
• Experiments show BART-large is robust to embedding degeneration during fine-tuning, and artificial isotropy methods do not improve performance and can disrupt natural robustness.
• Applying DefinitionEMB improves embedding isotropy and enhances performance on various NLP tasks for models like RoBERTa and BART, particularly for text summarization, by reducing frequency bias.

Analyzing Token Embeddings and Their Impact on Pre-trained LLMs

The paper "Reconsidering Token Embeddings with the Definitions for Pre-trained LLMs" by Zhang, Li, and Okumura investigates the efficacy of token embeddings in pre-trained LLMs (PLMs) and introduces a method to mitigate observed deficiencies. A central issue discussed in the paper is the degeneration of learned token embeddings into anisotropy, where embeddings become biased by token frequency and occupy a narrow cone-shaped distribution. This problem raises concerns about the semantic quality of embeddings, especially for low-frequency tokens, which are crucial in many NLP tasks.

Experimental Analysis of Fine-tuning Dynamics

The researchers focus first on the robustness of BART-large, a specific PLM, against degeneration in its fine-tuning phase. BART-large was chosen for its perceived resilience. Through rigorous testing across various datasets, the study reveals that while BART's embeddings do not easily degenerate, approaches aimed at artificially improving isotropy, such as the removal of specific vector directions, fail to genuinely enhance the model's performance. Instead, these techniques often disrupt the natural robustness seen during fine-tuning.

Introduction of DefinitionEMB

Addressing these embedding issues, the authors propose DefinitionEMB, a method leveraging definitions from Wiktionary to form token embeddings that are both isotropically distributed and enriched with semantic information. By utilizing comprehensive definitions alongside PLMs’ token embeddings, the study targets the semantic shortcomings specifically for rare tokens. Through exhaustive experiments, DefinitionEMB demonstrates performance improvements for models like RoBERTa-base and BART-large on tasks evaluated with datasets such as GLUE and text summarization benchmarks.

Empirical Results and Discussion

The results from employing DefinitionEMB indicate that its application leads to improved isotropy in representation and performance boosts on a spectrum of NLP tasks. For both RoBERTa and BART, replacing traditional token embeddings with those constructed via DefinitionEMB enhances model predictions, especially for text summarization tasks, as evidenced by increased ROUGE scores. The method effectively aligns the embeddings of both high-frequency and rare words, thus reducing frequency bias without resorting to degenerative isotropy enhancement tactics.

Theoretical and Practical Implications

The implications of this research stretch beyond its immediate improvements. The insights into embedding dynamics and the development of DefinitionEMB could pave the way for future models that inherently correct frequency biases, reducing the strain on downstream tasks’ fine-tuning. Particularly in scenarios involving rare or out-of-vocabulary tokens, DefinitionEMB promises robust performance by anchoring semantic understanding in a wider, uniformly distributed context.

Future Directions

Looking forward, integrating DefinitionEMB with other PLM architectures—such as decoder-only models—could yield further refinements. Moreover, investigating how these embeddings perform outside of the constraints of PLMs’ pre-definined vocabularies remains unexplored territory. Given the density of semantic overlap in current vocabulary constructs, exploring a broader application of this method could lead to the development of universally applicable NLP systems with less dependency on frequency-based adjustments.

By re-evaluating the methodology for generating and fine-tuning token embeddings within PLMs, Zhang, Li, and Okumura provide a foundation for more semantically coherent and frequency-agnostic linguistic models, supporting ongoing advancements in the field of artificial intelligence.