Semantic Drift Patterns in Continually Fine-Tuned Transformer Models

Abstract

Continual fine-tuning enables transformer models to adapt to evolving domain-specific data, but this
process also introduces gradual semantic drift, where internal representation structures shift away
from their pretrained general-language equilibrium. This study characterizes drift as a progressive
deformation of embedding geometry, attention allocation patterns, and contextual reasoning behavior
observed across sequential fine-tuning stages. Early drift enhances domain specialization, while
prolonged fine-tuning leads to contraction of semantic diversity, increased lexical rigidity, and
reduced cross-domain generalization. The findings demonstrate that semantic drift is not inherently
detrimental, but becomes problematic when representational realignment surpasses stability
thresholds that preserve conceptual grounding. Monitoring embedding-space coherence and attention
distribution stability offers a practical path to controlling drift and maintaining a balance between
adaptive specialization and linguistic robustness.