Papers
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Multimodal Data Processing
NetLLM: Adapting Large Language Models for Networking
Published:2/4/2024
LLM Adaptation for Networking TasksMultimodal Data ProcessingAdaptive Bitrate StreamingNetworking Prediction and OptimizationLow-Cost Fine-Tuning Framework
This study introduces the NetLLM framework, which adapts large language models to efficiently solve networking tasks, reducing engineering costs and improving generalization. In three specific applications, NetLLM outperforms existing stateoftheart algorithms.
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LEMUR: Large scale End-to-end MUltimodal Recommendation
Published:11/14/2025
Large-Scale Multimodal Recommendation SystemsEnd-to-End Recommender SystemsFederated Learning for Recommendation OptimizationCold-Start Problem MitigationMultimodal Data Processing
LEMUR is a largescale endtoend multimodal recommender system that addresses coldstart and generalization issues. By jointly optimizing multimodal and recommendation components, LEMUR adapts dynamically to new data while reducing computational costs, showing significant improv
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