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

2026-04-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Explainable AI · Depth: Advanced, quick

Summary

Large Language Models (LLMs) exhibit strong performance but suffer from opaque internal mechanisms, which impedes trustworthiness and safe deployment. While most explainable AI research focuses on post-hoc methods, intrinsic interpretability is an emerging alternative that integrates transparency directly into model architectures. This paper systematically reviews recent advances in intrinsic interpretability for LLMs, classifying approaches into five design paradigms: functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction. The review also discusses open challenges and future research directions in this field, with a comprehensive list of papers available on GitHub.

Key takeaway

For research scientists developing or deploying LLMs, understanding intrinsic interpretability is crucial for building more trustworthy and safer systems. You should explore design paradigms like concept alignment or explicit modularization to integrate transparency directly into your model architectures, moving beyond reliance on external post-hoc explanations. This shift can significantly improve model accountability and reduce deployment risks.

Key insights

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

Principles

• Transparency should be intrinsic, not post-hoc.
• Design paradigms enhance LLM interpretability.

Method

Approaches to intrinsic interpretability are categorized into functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction.

Topics

• Intrinsic Interpretability
• Large Language Models
• Explainable AI
• Model Architectures
• 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 Artificial Intelligence.