Analyzing Transformers in Embedding Space (2209.02535v3)

Abstract: Understanding Transformer-based models has attracted significant attention, as they lie at the heart of recent technological advances across machine learning. While most interpretability methods rely on running models over inputs, recent work has shown that a zero-pass approach, where parameters are interpreted directly without a forward/backward pass is feasible for some Transformer parameters, and for two-layer attention networks. In this work, we present a theoretical analysis where all parameters of a trained Transformer are interpreted by projecting them into the embedding space, that is, the space of vocabulary items they operate on. We derive a simple theoretical framework to support our arguments and provide ample evidence for its validity. First, an empirical analysis showing that parameters of both pretrained and fine-tuned models can be interpreted in embedding space. Second, we present two applications of our framework: (a) aligning the parameters of different models that share a vocabulary, and (b) constructing a classifier without training by ``translating'' the parameters of a fine-tuned classifier to parameters of a different model that was only pretrained. Overall, our findings open the door to interpretation methods that, at least in part, abstract away from model specifics and operate in the embedding space only.

• The paper introduces a new method for analyzing Transformer parameters by comparing fine-tuned and pre-trained states via difference vectors.
• It demonstrates that fine-tuning impacts vary across layers, with distinct shifts in sentiment recognition seen in classification heads and feedforward networks.
• The study highlights implications for developing more interpretable and efficient fine-tuning strategies in natural language processing.

Interpretation of Transformer Parameters in Embedding Space through Fine-tuning Analysis

Introduction to Parameter Interpretation in Transformers

Transformers have become a pivotal architecture in the domain of NLP, underpinning advancements in various tasks. A significant portion of research has been devoted to dissecting these models to understand the roles of their inner mechanisms. A novel approach to model interpretability, focusing on analyzing Transformers without inference or backpropagation, has emerged. Specifically, examining the behavior of models in the embedding space offers a fresh lens to interpret the Transformer components, including both the self-attention mechanism and feed-forward networks.

Fine-tuning Analysis in Embedding Space

Fine-tuning transformers on specific tasks like sentiment analysis modifies model parameters to adapt to task-specific nuances. An investigation into the fine-tuned model parameters, especially focusing on GPT-2, reveals interesting patterns. Through manual inspection of difference vectors (parameters post-fine-tuning minus pre-fine-tuning) across various model components, certain trends related to sentiment emerge distinctly in the embedding space.

Classification Head Parameters

The examination of fine-tuning vectors associated with the classification head reveals clear distinctions between positive and negative sentiment labels. Keywords associated with positive sentiments include terms related to appreciation and enjoyment like "amazing", "wonderful", and "love", contrasted with negative sentiments represented by terms such as "bullshit", "crap", and "inept".

Fine-tuning Dynamics across Layers

The fine-tuning impact varies across different layers and parameter groups within the Transformer model. While some layers show a pronounced shift toward recognizing sentiment-laden vocabulary, others exhibit minor or no significant changes. The diversity in these dynamics suggests a layered complexity in how sentiment analysis fine-tuning influences parameter adjustments in embedding space.

Feedforward Keys and Values

Considering feedforward network components, a nuanced understanding of parameter changes emerges. For instances labeled as positive, terms enhancing or acknowledging positive aspects are prominent. Conversely, parameters correlating with negative instances concentrate on derogatory or diminishing terms. This polarity in fine-tuning adjustments further underscores the Transformer's adaptability to sentiment tasks.

Implications and Future Directions

The insights gleaned from interpreting Transformer parameters in the embedding space highlight the model's sensitivity and adaptability to task-specific fine-tuning. This framework not only opens new avenues for model interpretation but also for the development of more nuanced and efficient fine-tuning strategies that leverage embedding space dynamics.

Further research could explore the granularity of these adjustments across different domains and tasks, expanding the understanding of contextual embedding space transformations. Additionally, extending this analysis framework to other Transformer variants and architectures could yield broader insights into the universality or specificity of these interpretability patterns.

Conclusion

The investigation into the interpretability of Transformer parameters within the embedding space, particularly through the lens of fine-tuning for sentiment analysis, presents a promising direction for understanding model behavior and adjustments. By deciphering the intricate patterns of parameter changes, this research contributes to demystifying the black box of Transformer models, enhancing the interpretability and applicability of these powerful tools in NLP.