Understanding Learning Dynamics Of Language Models with SVCCA (1811.00225v3)

Abstract: Research has shown that neural models implicitly encode linguistic features, but there has been no research showing \emph{how} these encodings arise as the models are trained. We present the first study on the learning dynamics of neural LLMs, using a simple and flexible analysis method called Singular Vector Canonical Correlation Analysis (SVCCA), which enables us to compare learned representations across time and across models, without the need to evaluate directly on annotated data. We probe the evolution of syntactic, semantic, and topic representations and find that part-of-speech is learned earlier than topic; that recurrent layers become more similar to those of a tagger during training; and embedding layers less similar. Our results and methods could inform better learning algorithms for NLP models, possibly to incorporate linguistic information more effectively.

• The paper demonstrates that LMs acquire linguistic features at different training stages, with POS learned early and topic features later.
• It employs SVCCA on a two-layer LSTM LM to compare hidden representations without relying on annotated data.
• Findings reveal that while recurrent layers quickly specialize in task-relevant signals, embedding layers maintain broader linguistic information.

Understanding Learning Dynamics Of LLMs with SVCCA

The paper "Understanding Learning Dynamics of LLMs with SVCCA" by Naomi Saphra and Adam Lopez investigates the evolving representations in neural LLMs (LMs) using Singular Vector Canonical Correlation Analysis (SVCCA). This work offers insights into how LMs internally represent linguistic features during the training process, bridging a gap in the existing literature that primarily focuses on the static analysis of trained models.

Key Methodology

At the heart of the paper is SVCCA, a technique that allows the comparison of learned neural representations across different stages of training and across different models. This approach is advantageous because it does not require annotated data, thus enhancing flexibility and applicability. SVCCA measures the correlation between hidden representations of neural networks and trained tagging models that predict specific categories, including syntactic, semantic, and global topics.

The authors employ a two-layer LSTM LM to analyze its learning dynamics. The comparison targets the hidden states of the LMs with corresponding layers of taggers trained on different linguistic properties, including part-of-speech (POS), semantic tags, and topic labels. This paper focuses on how quickly each layer learns particular linguistic tasks, leveraging SVCCA to assess similarities in learned representations.

Findings and Implications

The findings show that LMs learn linguistic categories at varying rates. Notably, part-of-speech tags are acquired early in the training, as evidenced by their strong correlation to LM representations. Semantic properties follow, while topic-related features, which require more global context, are represented later in the training process.

Importantly, the results indicate that recurrent layers of the LM diverge early in training, specializing in ways that benefit the LLMing task specifically, whereas embedding layers remain more uniform across tasks. This pattern suggests that embedding layers capture a broader range of linguistic features before specializing, which is a key consideration for designing more efficient learning algorithms.

Theoretical and Practical Implications

The paper provides a theoretical framework for understanding why neural models generalize effectively despite their capacity for memorizing data. By identifying that local lexical categories are learned earlier, it aligns with the hypothesis that effective language processing models mimic linguistic hierarchies. This understanding has practical implications for improving NLP systems. By facilitating the integration of linguistic information, the research encourages the development of models that focus on relevant linguistic features at optimal stages of training.

Future Directions

The authors point to several avenues for future work. One potential direction is to incorporate linguistic structure feedback into training dynamics actively. Additionally, exploring the phenomenon of gradient starvation—where frequently encountered features overshadow rarer ones—could yield insights into optimizing the representation learning process. Given the demonstrated reliability of SVCCA, it can be further leveraged to compare architectures with different initializations or explore layer-specific encoding of linguistic features in more complex models such as transformer-based architectures.

In conclusion, this paper effectively uses SVCCA to elucidate the inner workings of LMs during training, providing a more nuanced understanding of how different linguistic features are captured over time. This work informs improvements in both theoretical models and practical applications in NLP, with the potential to make neural models both more efficient and interpretable.