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

A new survey examines intrinsic interpretability approaches for Large Language Models, categorizing design methods that build transparency directly into model architectures rather than applying post-hoc explanations. The research identifies five key paradigms—functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction—addressing the critical challenge of making LLMs more trustworthy and safer for deployment.

The opacity of Large Language Models represents a significant obstacle to their wider adoption in high-stakes applications. While these models demonstrate impressive capabilities across natural language processing tasks, their black-box nature creates legitimate concerns about trustworthiness, accountability, and safe deployment. This survey shifts focus from the dominant post-hoc explanation paradigm—which applies external interpretation methods to trained models—toward intrinsic interpretability, where transparency is engineered into the fundamental architecture and computational processes themselves.

The field has increasingly recognized that bolting explanations onto existing models after training offers limited insight into actual decision-making mechanisms. Intrinsic interpretability represents a more principled approach, embedding interpretable components directly into model design. The survey's categorization of five design paradigms provides researchers and practitioners with a structured framework for understanding different architectural approaches to transparency.

For the AI industry, this research direction carries substantial implications. Organizations deploying LLMs in regulated sectors—finance, healthcare, legal—face mounting pressure to demonstrate model reasoning. Interpretable architectures could reduce liability exposure and facilitate regulatory compliance, particularly as governments implement AI governance frameworks. The shift toward built-in transparency may also accelerate adoption in mission-critical applications where current models face justified skepticism.

The research community now faces the challenge of balancing interpretability with performance. Future work must determine whether transparent architectures can match the capabilities of existing opaque models, and whether the five identified paradigms can effectively scale to frontier-scale language models. Success here could fundamentally reshape how AI systems are developed and deployed.

• →Intrinsic interpretability builds transparency directly into LLM architectures rather than relying on post-hoc explanation methods applied after training.
• →Five design paradigms—functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction—provide a structured framework for interpretable model development.
• →Transparent architectures could significantly reduce liability and regulatory compliance challenges for organizations deploying LLMs in high-stakes domains.
• →The critical challenge ahead involves balancing interpretability improvements with maintaining the performance levels of current state-of-the-art models.
• →This research direction aligns with growing institutional and regulatory pressure for AI systems to provide verifiable explanations of their reasoning processes.