Grokking of Hierarchical Structure in Vanilla Transformers (2305.18741v1)

Abstract: For humans, language production and comprehension is sensitive to the hierarchical structure of sentences. In natural language processing, past work has questioned how effectively neural sequence models like transformers capture this hierarchical structure when generalizing to structurally novel inputs. We show that transformer LLMs can learn to generalize hierarchically after training for extremely long periods -- far beyond the point when in-domain accuracy has saturated. We call this phenomenon \emph{structural grokking}. On multiple datasets, structural grokking exhibits inverted U-shaped scaling in model depth: intermediate-depth models generalize better than both very deep and very shallow transformers. When analyzing the relationship between model-internal properties and grokking, we find that optimal depth for grokking can be identified using the tree-structuredness metric of \citet{murty2023projections}. Overall, our work provides strong evidence that, with extended training, vanilla transformers discover and use hierarchical structure.

Structural Grokking in Vanilla Transformers: An Analysis of Hierarchical Structure Capture

This essay offers an expert overview of the academic paper "Grokking of Hierarchical Structure in Vanilla Transformers," which investigates the potential of transformer models to internalize and utilize hierarchical structures inherent in human language through extended training. The core assertion of this paper is the identification of a phenomenon termed "structural grokking," wherein transformers gradually enhance their capacity to generalize hierarchically long after in-domain accuracy has stagnated.

The research applies empirical analysis to assess the capacity of vanilla transformers, a prevalent architecture in NLP, to exhibit structural grokking. This phenomenon notably occurs through prolonged training, beyond conventional stopping points where validation performance usually plateaus. The paper contends that previous underestimations of transformer generalization capabilities stem from premature termination of training phases based solely on in-domain performance metrics.

Main Findings

The paper demonstrates that transformers, when trained for extended periods, can indeed learn to harness hierarchical sentence structures. Key findings include:

1. Structural Grokking Phenomenon: The paper documents "structural grokking" across multiple datasets, characterized by enhanced hierarchical generalization following extensive training beyond typical stopping points.
2. Inverted U-Shaped Scaling: Structural grokking exhibits an inverted U-shaped relationship with model depth. Intermediate-depth models significantly outperform both very shallow and very deep configurations. This insight reveals optimal model configurations for maximizing hierarchical generalization capabilities.
3. Predictive Model Characteristics: The paper evaluates internal model properties, such as weight norms, attention sparsity, and functional tree-structuredness, to predict conditions under which grokking occurs. The research identifies tree-structuredness as a reliable predictor of grokking, distinguishing itself from properties like weight norms and attention sparsity that increase consistently with depth.
4. Empirical Evidence Against Prior Claims: Findings directly challenge assertions from previous work that transformers inherently lack bias towards hierarchical organization. By surpassing conventional validation phases, transformers achieve marked improvements in generalization, reaching accuracies upwards of 80% in some configurations previously reported to perform poorly.

Practical and Theoretical Implications

The implications of these results extend to both practical applications in NLP and broader theoretical considerations. Practically, the awareness of structural grokking can prompt more informed training regimes that avoid premature stopping, potentially yielding models with superior generalization abilities and applications in diverse linguistic tasks. Theoretically, this research suggests that transformers may possess stronger inductive biases towards structure than previously assumed, opening discourse on the architectures' capacity to approximate aspects of human-like language processing.

Speculation on Future Directions

This investigation paves the way for new inquiries into whether similar structural grokking phenomena emerge in larger, diverse datasets and across various language typologies beyond the evaluated English-based datasets. Further exploration could involve assessing the relationship between dataset size, model architecture refinements, and the dynamics of structural grokking. Delving into these areas may elucidate how models could be better aligned with innate linguistic structures and cognitive processes.

Conclusion

The research illuminates critical facets of transformer functionality, emphasizing the capacity of current architectures to achieve surprisingly effective levels of hierarchical structure capture with extended training. This knowledge challenges existing perceptions and highlights the potential for fine-tuning transformer configurations to unlock enhanced capabilities in NLP applications.