StructLens: A Structural Lens for Language Models via Maximum Spanning Trees
arXiv:2603.03328v1 Announce Type: new Abstract: Language exhibits inherent structures, a property that explains both language acquisition and language change. Given this characteristic, we expect language models to manifest internal structures as well. While interpretability research has investigated the components of language models, existing approaches focus on local inter-token relationships within layers or modules (e.g., Multi-Head Attention), leaving global inter-layer relationships largely overlooked. To address this gap, we introduce StructLens, an analytical framework designed to reveal how internal structures relate holistically through their inter-token connection within a layer. StructLens constructs maximum spanning trees based on the semantic representations in residual streams, analogous to dependency parsing, and leverages the tree properties to quantify inter-layer distance (or similarity) from a structural perspective. Our findings demonstrate that StructLens yields
arXiv:2603.03328v1 Announce Type: new Abstract: Language exhibits inherent structures, a property that explains both language acquisition and language change. Given this characteristic, we expect language models to manifest internal structures as well. While interpretability research has investigated the components of language models, existing approaches focus on local inter-token relationships within layers or modules (e.g., Multi-Head Attention), leaving global inter-layer relationships largely overlooked. To address this gap, we introduce StructLens, an analytical framework designed to reveal how internal structures relate holistically through their inter-token connection within a layer. StructLens constructs maximum spanning trees based on the semantic representations in residual streams, analogous to dependency parsing, and leverages the tree properties to quantify inter-layer distance (or similarity) from a structural perspective. Our findings demonstrate that StructLens yields an inter-layer similarity pattern that is distinctively different from conventional cosine similarity. Moreover, this structure-aware similarity proves to be beneficial for practical tasks, such as layer pruning, highlighting the effectiveness of structural analysis for understanding and optimizing language models. Our code is available at https://github.com/naist-nlp/structlens.
Executive Summary
The article introduces StructLens, a novel analytical framework designed to uncover internal structural relationships in language models by constructing maximum spanning trees from semantic representations in residual streams. Unlike existing interpretability methods that focus on local intra-layer interactions, StructLens addresses the overlooked global inter-layer dynamics by modeling dependencies akin to linguistic parsing. The framework quantifies inter-layer similarity via structural configurations, revealing patterns distinct from conventional cosine similarity. Empirical results indicate that this structure-aware similarity enhances practical applications, particularly in layer pruning, offering a more nuanced lens for model optimization. The availability of open-source code supports reproducibility and further research.
Key Points
- ▸ Introduction of StructLens as a structural lens for language models
- ▸ Utilization of maximum spanning trees to model inter-layer dependencies
- ▸ Distinctive inter-layer similarity pattern identified via structural analysis
Merits
Innovative Framework
StructLens fills a critical gap by introducing a structural perspective to language model analysis, moving beyond local inter-token relationships to global inter-layer structures.
Demerits
Scope Limitation
While StructLens offers structural insights, it currently lacks comparative validation against alternative structural modeling approaches, potentially limiting broader applicability.
Expert Commentary
StructLens represents a meaningful advancement in the interpretability domain by aligning linguistic theory with computational modeling. The analogical use of dependency parsing via maximum spanning trees is both conceptually elegant and practically significant. By quantifying structural inter-layer relationships, the authors provide a more holistic view of model architecture, which could inform better design choices in future language models. Moreover, the distinction between structural similarity and conventional cosine similarity suggests that current evaluation metrics may be insufficient for capturing architectural coherence. This work bridges interdisciplinary knowledge—combining linguistic theory with machine learning—and opens a pathway for more sophisticated analytical tools. As the field progresses, StructLens may inspire similar structural-based interpretability frameworks across domains beyond NLP.
Recommendations
- ✓ 1. Extend StructLens to include comparative studies with alternative structural modeling techniques to validate its unique contributions.
- ✓ 2. Integrate structural similarity metrics into mainstream interpretability dashboards for broader adoption and practical impact.