Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures
While Large Language Models (LLMs) have achieved strong performance across many NLP tasks, their opaque internal mechanisms hinder trustworthiness and safe deployment. Existing surveys in explainable AI largely focus on post-hoc explanation methods that interpret trained models through external approximations. In contrast, intrinsic interpretability, which builds transparency directly into model architectures and computations, has recently emerged as a promising alternative. This paper presents a systematic review of the recent advances in intrinsic interpretability for LLMs, categorizing existing approaches into five design paradigms: functional transparency, concept alignment, represent...
| Authors | Yutong Gao, Qinglin Meng, Yuan Zhou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 April 2026 |
| Last updated at source | 20 April 2026 |
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