Similarity Analysis of Contextual Word Representation Models

Abstract: This paper investigates contextual word representation models from the lens of similarity analysis. Given a collection of trained models, we measure the similarity of their internal representations and attention. Critically, these models come from vastly different architectures. We use existing and novel similarity measures that aim to gauge the level of localization of information in the deep models, and facilitate the investigation of which design factors affect model similarity, without requiring any external linguistic annotation. The analysis reveals that models within the same family are more similar to one another, as may be expected. Surprisingly, different architectures have rather similar representations, but different individual neurons. We also observed differences in information localization in lower and higher layers and found that higher layers are more affected by fine-tuning on downstream tasks.

• The paper introduces traditional and novel similarity metrics to evaluate how neural architectures encode linguistic information at multiple levels.
• It finds that lower layers across models exhibit higher similarity while higher layers become more task-specific and localized.
• The study highlights that fine-tuning focusing on mutable higher layers while freezing lower ones can achieve efficiency with minimal performance trade-off.

An Evaluation of Contextual Word Representation Models Through Similarity Analysis

The paper "Similarity Analysis of Contextual Word Representation Models" presents a thorough investigation into the internal representations of contextual word models, such as ELMo, BERT, GPT, GPT2, and XLNet. The researchers employ various similarity measures to uncover how different modeling choices impact the information encoded by these models at a granular level. This work explores how different neural architectures hold or distribute information across layers and neurons, and it seeks to align model behaviors across divergent architectures.

The authors introduce and utilize both traditional and novel similarity measures, examining models at neuron, representation, and attention levels. This examination enables insight into the level of localized versus distributed information representation across different models and layers. Specifically, the paper analyzes similarities without reliance on external linguistic annotations. This methodological decision allows direct comparison purely based on model constructs and behaviors.

Key Insights and Results

1. Intra-Model Versus Inter-Model Similarities: The analysis reveals that models within the same architectural family tend to display higher similarity in their representations. Notably, different architectures can have similar overall representations but diverge significantly at the individual neuron level. This finding implies that while models encode similar information, the allocation of this information across neurons can vary dramatically based on model architecture.
2. Layer-Specific Observations: Lower layers across different models tend to exhibit higher similarity compared to their higher layers. Higher layers are more susceptible to fine-tuning and task-specific modifications, suggesting they hold more task-relevant information. This characteristic is essential for understanding model generalization and adaptation capacities during fine-tuning.
3. Localization in Model Representations: The analysis indicates that higher layers exhibit increased localization of information compared to lower layers. This suggests that as models process input data through their hierarchical layers, the representations become more concentrated or specific to particular neurons, potentially lending to more distilled high-level features.
4. Effect of Fine-Tuning: Fine-tuning predominantly alters higher layers, reducing their similarity to corresponding layers in pre-trained models. However, the study finds that freezing the lower layers during fine-tuning can maintain comparable performance, which indicates potential paths for efficient training methodologies.
5. Implications for Efficient Model Training: Utilizing the findings on layer sensitivity, the authors propose an efficient fine-tuning strategy that focuses computational resources on the most malleable layers, thereby reducing computational costs without a significant performance trade-off.

Implications and Future Directions

The research augments our understanding of how information is encoded across different architectures and provides new lenses to evaluate and interpret neural models. By understanding models' behavior at a granular level, we can enhance model interpretability and develop more efficient training strategies; these are critical for advancing both the theoretical frameworks and practical applications in natural language processing.

Future research could explore integrating these findings with probing techniques to further delineate the specific linguistic properties captured by each representation, or extend this analytical framework to interpret model behavior during other training phases. Additionally, the work posits interesting questions about the potential of neural representations to converge towards human-like understanding of language by identifying universal representation characteristics across varied models.

By contributing novel insights into the similarities in contextual word representations, this work opens up new possibilities for synergistic learning where cross-architecture knowledge can be pooled to enhance model understanding and performance.