A two-step in-context demonstration selection method, based on distance similarity and zero-shot prediction error, improves LLM-based mobile traffic prediction on a real 5G dataset compared with zero-shot and simple baselines.
RL meets multi-link operation in IEEE 802.11 be: Multi- headed recurrent soft-actor critic-based traffic allocation,
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Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection
A two-step in-context demonstration selection method, based on distance similarity and zero-shot prediction error, improves LLM-based mobile traffic prediction on a real 5G dataset compared with zero-shot and simple baselines.