A Transformer with Stack Attention (2405.04515v2)

Abstract: Natural languages are believed to be (mildly) context-sensitive. Despite underpinning remarkably capable LLMs, transformers are unable to model many context-free language tasks. In an attempt to address this limitation in the modeling power of transformer-based LLMs, we propose augmenting them with a differentiable, stack-based attention mechanism. Our stack-based attention mechanism can be incorporated into any transformer-based LLM and adds a level of interpretability to the model. We show that the addition of our stack-based attention mechanism enables the transformer to model some, but not all, deterministic context-free languages.

• The paper introduces a novel stack-based attention mechanism that augments transformers to effectively model nested and hierarchical language structures.
• It integrates a dedicated stack layer executing push, no-op, and pop operations alongside standard self-attention to mimic context-free grammar processing.
• Empirical results demonstrate improved performance on deterministic context-free tasks, highlighting potential applications in code parsing and legal document analysis.

Enhancing Transformers with Stack-Based Attention

Introduction to Stack-Based Attention for Transformers

Deep learning models, particularly transformers, have radically changed the landscape of NLP. However, despite their success, transformers often struggle with tasks that involve maintaining and manipulating a hierarchical structure, such as understanding nested or recursive language patterns. One well-known example in this context is the Dyck-$n$ language task, which involves correctly balancing nested structures and is a challenge for typical transformer architectures. This limitation stems from the inability of standard transformers to effectively model context-free grammars (CFGs), which play a crucial role in capturing the syntactic structure of languages.

The Stack-Based Attention Mechanism

A novel approach proposed to overcome this limitation is the introduction of a stack-based attention mechanism. The idea here is to augment the transformer model with a mechanism akin to a stack - an abstract data type that follows the Last In, First Out (LIFO) principle. This mechanism provides a way for the transformer to "remember" and "track" nested structures through operations that mimic pushing to and popping from a stack.

• Adding a Stack Layer: The approach integrates a stack attention sub-layer at each transformer layer. This sub-layer operates alongside the standard multi-head self-attention and feed-forward layers but focuses on emulating stack operations that are crucial for CFGs.
• Functionality of Stack Operations: The stack layer can execute three primary operations - push, no-op, and pop. These help in tracking and reverting back to previous states in a structured manner, which is a core requirement for parsing nested dependencies.

Practical Implications and Theoretical Contributions

The introduction of the stack-based mechanism in transformers addresses a significant gap in the model's ability to handle context-free language tasks. This augmentation not only enhances the transformer's theoretical capabilities but also shows practical improvements in specific CFG-related tasks.

• Empirical Improvements: The modified transformer demonstrates improved performance on several deterministic context-free (DCF) tasks compared to standard transformers.

One immediate application of this enhanced capability is in fields requiring nuanced language understanding, such as code parsing or processing complex legal documents. In these domains, the nested or hierarchical structure is prevalent, and the enhanced model could provide much more reliable interpretations than currently possible with standard transformer models.

Future Directions and Speculation

While the stack-augmented transformer shows promise, it is not without limitations. It still struggles with certain CFG tasks, particularly those involving modular arithmetic. This opens up several avenues for future research:

• Further Model Enhancements: Exploring ways to extend the stack mechanism to handle non-deterministic context-free languages could make the model even more powerful.
• Improvement in Efficiency: Currently, the stack-based model sacrifices some of the transformer's parallel processing capabilities, affecting its efficiency. Finding ways to retain parallelism while accommodating stack functionalities could be a crucial area for improvement.
• Integration and Compatibility: Ensuring that this new architecture can seamlessly integrate with existing pre-trained models without requiring extensive modifications will be key to its adoption.

Conclusion

The development of a stack-augmented transformer represents an exciting step forward in the pursuit of more sophisticated AI LLMs. By marrying the strengths of traditional transformers with the capabilities of stack-based processing, researchers have opened up new possibilities for tackling complex linguistic structures that were previously out of reach. This progress underscores the continual evolution of AI models to mimic, and eventually perhaps even replicate, the nuanced understanding of human language.