5 citations · 20 across the 21 of their papers we have counts for
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cs.AI2024★ 1 cited
ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy
Zonghan Yang, Peng Li, Ming Yan +3
Language agents have demonstrated autonomous decision-making abilities by reasoning with foundation models. Recently, efforts have been made to train language agents for performanc…
cs.AI2024★ 4 cited
Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
Weizhou Shen, Chenliang Li, Hongzhan Chen +5
Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs, empowering them to interact with external tools (e.g., APIs, functions) and complete var…