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

Intrinsic interpretability is fundamentally different from current explanation methods: Rather than trying to understand LLMs after they're built (post-hoc explanations), intrinsic interpretability builds transparency directly into how models are designed and operate from the ground up, offering a more reliable path to trustworthy AI systems.

Five distinct architectural approaches are emerging to make LLMs more transparent: The survey identifies functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction as key design paradigms—meaning researchers are developing multiple complementary strategies rather than relying on a single solution.

Current LLMs lack sufficient transparency for safe deployment: The paper emphasizes that the "opaque internal mechanisms" of today's large language models create barriers to trustworthiness, suggesting that without intrinsic interpretability improvements, responsible deployment of these systems remains challenging.

This is an emerging field with significant open challenges ahead: By framing intrinsic interpretability as a "recent" and "emerging" area with outlined future research directions, the survey indicates that while progress is being made, we're still in early stages of solving the interpretability problem for LLMs.