Where Do LLMs Compose Meaning? A Layerwise Analysis of Compositional Robustness.
| Authors | Nura Aljaafari et al. |
| Year | 2026 |
| Venue | EACL 2026 |
| Paper | View on ACL Anthology |
| PDF | Download |
Abstract
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Engineering Breakdown
Plain English
This paper investigates where and how large language models build compositional meaning—the ability to combine simple concepts into complex ones—across their internal layers. The researchers conduct a layerwise analysis to understand which layers are responsible for composing meaning and test how robust this composition process is to perturbations, providing insights into the internal mechanisms that allow LLMs to handle complex linguistic structures.
Key Engineering Insight
Different layers in LLMs specialize in different aspects of composition, and this layered specialization can be disrupted by targeted perturbations—meaning composition isn't a monolithic process but distributed across depth, which has direct implications for model robustness and interpretability.
Why It Matters for Engineers
Understanding where composition happens layer-by-layer helps engineers diagnose failure modes in production LLMs, design better pruning and distillation strategies, and build more robust systems against adversarial inputs or domain shift. It also informs architectural decisions about which layers to monitor, finetune, or protect in deployment.
Research Context
Prior work on LLM interpretability showed that different layers encode different information, but systematic understanding of compositional robustness across layers was missing. This paper fills that gap by providing a methodology to identify which layers are critical for meaning composition and how they fail, advancing the field beyond simple layer-probing toward actionable robustness analysis that production teams can apply.
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