Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Advanced, extended

Summary

This survey systematically reviews recent advancements in intrinsic interpretability for Large Language Models (LLMs), a field focused on building transparency directly into model architectures rather than relying on post-hoc explanations. While LLMs like those described in Brown et al., 2020, and Chowdhery et al., 2022, achieve strong performance, their opaque nature poses trust and safety risks, particularly in high-stakes applications. The paper categorizes existing intrinsic interpretability approaches into five design paradigms: functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction. It distinguishes intrinsic methods from post-hoc techniques, which often suffer from a fidelity gap, by emphasizing structural fidelity. The authors also discuss open challenges and future research directions, providing a unified framework for understanding how different mechanisms contribute to LLM transparency.

Key takeaway

For research scientists developing Large Language Models, focusing on intrinsic interpretability design principles is crucial for building trustworthy and deployable systems. You should explore architectural choices that embed transparency directly, such as explicit modularization or latent sparsity induction, to mitigate the fidelity gaps inherent in post-hoc explanation methods. Prioritize structural fidelity to ensure that model behavior directly corresponds to its explanation, enhancing safety in high-stakes applications.

Key insights

Intrinsic interpretability builds transparency directly into LLM architectures to enhance trustworthiness and safety.

Principles

• Transparency should be an inherent model property.
• Interpretability and performance are not mutually exclusive.
• Structured representations improve model transparency.

Method

Intrinsic interpretability methods are categorized into five design paradigms: functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction.

In practice

• Implement modularity in LLM designs.
• Incorporate sparsity into training objectives.
• Align model representations with human concepts.

Topics

• Large Language Models
• Intrinsic Interpretability
• Explainable AI
• Post-hoc Explanation
• Model Design Paradigms

Code references

• PKU-PILLAR-Group/Survey-Intrinsic-Interpretability-of-LLMs

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.