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In this work, we compare emergent communication (EC) built upon multi-agentdeep reinforcement learning (MADRL) and language-oriented semanticcommunication (LSC) empowered by a pre-trained large language model (LLM) usinghuman language. In a multi-agent remote navigation task, with multimodal inputdata comprising location and channel maps, it is shown that EC incurs hightraining cost and struggles when using multimodal data, whereas LSC yields highinference computing cost due to the LLM's large size. To address theirrespective bottlenecks, we propose a novel framework of language-guided EC(LEC) by guiding the EC training using LSC via knowledge distillation (KD).Simulations corroborate that LEC achieves faster travel time while avoidingareas with poor channel conditions, as well as speeding up the MADRL trainingconvergence by up to 61.8% compared to EC.

Knowledge Distillation from Language-Oriented to Emergent Communication for Multi-Agent Remote Control