Where Do LLMs Compose Meaning? A Layerwise Analysis of Compositional Robustness

AbstractUnderstanding how large language models (LLMs) process compositional linguistic structures is integral to enhancing their reliability and interpretability. We present Constituent-Aware Pooling (CAP), a methodology grounded in compositionality, mechanistic interpretability, and information theory that intervenes in model activations by pooling token representations into linguistic constituents at various layers. Experiments across eight models (124M-8B parameters) on inverse definition modelling, hypernym and synonym prediction reveal that semantic composition is not localised to specific layers but distributed across network depth. Performance degrades substantially under constituent-based pooling, particularly in early and middle layers, with larger models showing greater sensitivity. We propose an information-theoretic interpretation: transformers’ training objectives incentivise deferred integration to maximise token-level throughput, resulting in fragmented rather than localised composition. These findings highlight fundamental architectural and training constraints requiring specialised approaches to encourage robust compositional processing.