Bridging the Semantic Gap in 6G: Tiny Language Models Under the Latency-Accuracy-Size Trilemma

Abstract

Sixth-generation (6G) wireless networks are expected to serve as AI-native infrastructure, transmitting meaning rather than mere bits -- a shift that makes semantic communication the central paradigm for next-generation connectivity. Deep learning-based semantic encoders show compelling gains in bandwidth efficiency; however, their dependence on large transformer models with hundreds of millions of parameters is at odds with the sub-millisecond latency, microjoule energy budgets, and kilobyte memory footprints of the constrained IoT and edge devices that will dominate 6G endpoints. Tiny language models (t-LMs) -- compact, quantised, task-specialised models deployable on microcontrollers, mobile system-on-chips, and edge accelerators -- are the enabling technology for closing this gap. This review provides a unified treatment of (i) the theoretical foundations of semantic information, covering semantic entropy, channel capacity, and rate-distortion theory; (ii) a two-axis taxonomy of t-LM-based semantic communication systems across five architecture classes and six compression paradigms; (iii) a survey of model compression techniques -- quantisation, pruning, knowledge distillation, low-rank adaptation, split computing, and neural architecture search -- through the lens of semantic quality preservation; and (iv) semantic-aware resource allocation frameworks for 6G multi-user networks. Evidence across the surveyed literature shows that compression can reduce semantic encoder size by up to 99.98% while preserving task accuracy, that split computing achieves device-side encoders with as few as 640 parameters, and that knowledge graph integration cuts transmission energy by 65%. Seven open challenges are identified, spanning theoretical gaps, system design, knowledge-base management, post-quantum security, and hardware co-design, with a 3GPP standardisation roadmap toward IMT-2030.