Transformers and Attention Mechanisms Can Be Viewed as Additive Processes, Revealing New Insights into Model Interpretation

Transformers, a cornerstone of modern AI, are being reinterpreted as "fancy addition machines" through the lens of mechanistic interpretation, a growing subfield focused on reverse-engineering neural networks to decode their internal logic. Unlike traditional explainability methods like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), which analyze feature contributions at a high level, mechanistic interpretation dives into how specific neurons and layers process information, tracing how features evolve through the network. This approach aims to translate complex AI systems into human-readable algorithms, offering deeper insights into their functionality. The article explains that transformers rely on attention mechanisms to weigh input relationships. In multi-head attention, queries (Q), keys (K), and values (V) are split into multiple "heads," each processing data independently. Traditionally, these heads are concatenated after computation, but the author argues this can be reframed as an additive process. By summing outputs across heads instead of concatenating, the model effectively combines contributions from each head through addition, maintaining mathematical equivalence to the standard approach. This shift highlights how transformers aggregate information: instead of stacking parallel computations, they sum them, treating each head as a "channel" akin to convolutional neural networks (CNNs), where filters’ outputs are summed across layers. The technical breakdown reveals that the linear projection step (Wₒ) in traditional models can be viewed as slicing weights across the D dimension (hidden size) and summing contributions from each head. For instance, if each head’s output is a 64-dimensional vector, summing across 8 heads (H=8) reconstructs the original 512-dimensional space. This equivalence arises because the weights in the additive framework are subsets of those in the concatenated version, ensuring no loss of information. The author emphasizes that this perspective simplifies understanding transformers as systems that build outputs by iteratively adding features across layers, rather than relying on complex parallel operations. This reimagining opens new avenues for "circuit tracing," a method to track how specific features are learned and transformed through the network. By framing attention as additive, researchers can more easily map how individual neurons contribute to final predictions, potentially improving model transparency and debugging. The article also notes that this approach aligns with broader efforts to demystify deep learning, particularly as models grow larger and more opaque. Evaluation and Context The insight challenges conventional views of attention mechanisms, offering a simpler framework for analyzing transformers. Industry experts may find this perspective valuable for optimizing model design and interpretability. While the article focuses on theoretical equivalence, its practical implications could influence future AI research, especially in aligning human understanding with neural network behavior. The author’s work underscores the importance of mechanistic interpretation in advancing AI explainability, a critical area as models like transformers become ubiquitous in applications. The paper and blog referenced suggest this is part of a broader movement to bridge the gap between AI’s complexity and human comprehension, with potential impacts on both academia and industry.