Bridging Linguistic Structure and Mechanistic Interpretability for Conceptual Interpretation in Language Models

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Definitional Semantic Role Analysis (DSRA) is a new methodology for interpreting language models' conceptual composition. It addresses how models map definitional expressions to abstract meaning. DSRA applies causal tracing in a reverse dictionary task. It augments restoration traces with definitional semantic roles (DSRs) from Argument Structure Theory. This linguistic overlay identifies compositional functions (e.g., genus, differentia quality) linked to high-recovery states. It extends activation patching beyond token-level localization. Applied to GPT-J-6B (English) and BERTIN GPT-J-6B (Spanish), results show MLP layers link content-bearing tokens to high-specificity DSR categories early. MHA layers distribute integration across middle-to-upper layers, concentrating at the final token. Hidden states aggregate information in upper layers. This reveals systematic correspondence between internal activations and definitional structure across both languages.

Key takeaway

For NLP Engineers and AI Scientists focused on language model interpretability, this research offers a novel framework for understanding conceptual composition. If you design or evaluate models for nuanced semantic understanding, DSRA's insights into MLP and MHA layer functions can inform your architectural choices and debugging strategies. This work suggests that integrating linguistic structure into mechanistic interpretability can provide a clearer picture of how models build meaning.

Key insights

DSRA methodology links language model internal activations to definitional semantic roles, revealing how conceptual meaning is composed across layers.

Principles

• MLP layers map content tokens to DSRs early.
• MHA layers integrate information mid-to-upper layers.
• Upper layers aggregate hidden state information.

Method

DSRA uses causal tracing in a reverse dictionary task, augmenting restoration traces with definitional semantic roles (DSRs) to identify compositional functions and extend activation patching.

Topics

• Language Models
• Mechanistic Interpretability
• Conceptual Interpretation
• Causal Tracing
• Definitional Semantic Role Analysis
• GPT-J-6B

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.