SkillCom: Decomposing LLM-based Semantic Communication into Task and Channel Aware Skills

TL;DR

SkillCom introduces a modular framework for LLM-based semantic communication, decomposing it into explicit skills to improve robustness, diagnosability, and flexibility over traditional monolithic approaches.

Contribution

It proposes a novel skill decomposition approach for semantic communication with explicit interfaces, enabling targeted repair and better robustness.

Findings

SkillCom outperforms monolithic LLM baselines in multi-hop question answering.

It remains more robust under varying channel conditions.

Task-dependent skill preferences are observed in experiments.

Abstract

Large language models (LLMs) are increasingly used as semantic encoders and decoders in semantic communication. However, current LLM based systems mostly remain monolithic: a single prompted model, or a tightly coupled transmitter/receiver pair, must jointly perform semantic encoding, channel adaptation, and semantic decoding. Such coupling makes intermediate decisions difficult to control, diagnose, or replace, and may cause channel corruption to propagate through a compressed source representation. To address the limitations, we propose \textbf{SkillCom}, a modular framework that decomposes LLM-based semantic communication into four explicit skills: semantic abstraction skill, channel-adaptive transmission skill, receiver-side repair skill, and task execution skill. These skills are interconnected through typed semantic-unit interfaces. Thus, transmission operates on structured unit-level representations rather than on one monolithic text block. This design localizes channel impairment, enables targeted repair from successfully received units, and supports stage-wise ablation and single-skill replacement under matched communication constraints. Experiments on multi-hop question answering and dialogue state tracking show that SkillCom consistently outperforms the monolithic LLM baseline, remains more robust under varying channel conditions, and exhibits task-dependent preferences over skill realizations. The results suggest that explicit skill decomposition provides a more robust and diagnosable foundation for LLM-based semantic communication than monolithic methods.