161 citations · 161 across the 12 of their papers we have counts for
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cs.AI2024
CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning
Duo Wu, Jinghe Wang, Yuan Meng +3
Utilizing large language models (LLMs) for tool planning has emerged as a promising avenue for developing general AI systems, where LLMs automatically schedule external tools (e.g.…
cs.NI2024★ 161 cited
NetLLM: Adapting Large Language Models for Networking
Duo Wu, Xianda Wang, Yaqi Qiao +4
Many networking tasks now employ deep learning (DL) to solve complex prediction and optimization problems. However, current design philosophy of DL-based algorithms entails intensi…