Large Language Models (LLMs) exhibit strong performance across language tasks, raising the question of whether this success reflects linguistic abstraction or reliance on surface-level statistical patterns. This survey reviews recent advances in mechanistic interpretability (MI) for analyzing candidate internal mechanisms underlying linguistic competence in LLMs. We trace the shift from model-agnostic analyses to transformer-specific methods and summarize core approaches including probing, neuron- and circuit-level analysis, and causal interventions. Synthesizing empirical findings, we argue that current evidence supports an intermediate view: LLMs appear to use reusable mechanisms for some grammatical behaviors, while these mechanisms remain frequency-conditioned, distributed, and only partially understood. We further review interpretability studies of multilingual models, highlighting evidence for both language-specific and shared representations. Finally, we outline open challenges in scalability, benchmarking, and ethics, and discuss directions toward a more cautious mechanistic account of language in LLMs.