Distributed Specialization: How Transformers Process Rare Tokens Through Parameter Differentiation

Large language models (LLMs) often underperform on rare token prediction, even though such tokens carry critical information in specialized domains [1]. Prior work has identified frequency-sensitive neurons that modulate token logits [2], but the organizational principles underlying these neurons’ functional roles remain poorly understood. In this work, we analyze how transformers self-organize computation for rare versus common tokens, conducting a systematic neuron-level study across GPT-2 and Pythia model families. Our approach combines neuron ablation, activation-space analysis, and weight-space spectral characterization to reveal how functional specialization emerges dynamically during training. Neuron Influence Analysis and Training Dynamics. We measure each neuron’s contribution via the loss increase after ablation for the MLP layer before the unembedding layer and uncover a three-regime structure in influence distributions for rare token prediction: (1) a specialist plateau of highly influential neurons, (2) a power-law regime of moderately contributing neurons, and (3) a rapid-decay regime of minimally relevant neurons. In contrast, common tokens exhibit only the power-law and decay regimes, demonstrating that rare tokens recruit additional high-influence neurons. Tracking model states during training shows that these plateau neurons emerge progressively, suggesting spontaneous functional differentiation rather than pre-defined specialization. Distributed Specialization vs. Modularity. We next ask whether specialist neurons form modular clusters or operate within a distributed computation framework. Using graph-based community detection [3] in activation space, we find that these neurons are spatially dispersed across the last MLP layer, with modularity scores close to random baselines (Q = 0.05–0.11 vs. control Q = 0.04–0.09, p > 0.4). Attention-head ablation further shows that rare token processing does not depend on specialized routing: removing individual heads yields minimal performance impact (~7–8%), whereas full-layer ablation causes large drops (~42–45%). Despite their spatial distribution, specialist neurons exhibit structured co-activation and reduced effective dimensionality, revealing coordinated computation without topological modularity. Weight Space Analysis. To understand how specialization emerges, we analyze neuron weight spectra using Heavy-Tailed Self-Regularization theory [4]. Rare token neurons develop heavier-tailed weight distributions than controls (Hill α = 1.57–4.30 vs. 6.37–9.33), indicating stronger functional specialization. This spectral separation grows throughout training, suggesting that implicit regularization mechanisms drive the observed differentiation. Our findings reveal that transformers achieve rare token specialization through distributed parameter differentiation rather than modular organization. By recruiting additional high-influence neurons that are spatially dispersed yet functionally coordinated, models preserve context-sensitive flexibility while allocating adaptive capacity for rare token processing. These results challenge modular views of neural computation and provide insights for interpretable model editing and efficiency optimization.