7 citations · 8 across the 4 of their papers we have counts for
4 papers
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems
Yilun Kong, Jingqing Ruan, Yihong Chen +9
Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend…
Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain
Guanghu Sui, Zhishuai Li, Ziyue Li +4
The previous state-of-the-art (SOTA) method achieved a remarkable execution accuracy on the Spider dataset, which is one of the largest and most diverse datasets in the Text-to-SQL…
Learning to Collaborate by Grouping: a Consensus-oriented Strategy for Multi-agent Reinforcement Learning
Jingqing Ruan, Xiaotian Hao, Dong Li +1
Multi-agent systems require effective coordination between groups and individuals to achieve common goals. However, current multi-agent reinforcement learning (MARL) methods primar…
Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems
Bin Zhang, Hangyu Mao, Lijuan Li +4
Asynchronous action coordination presents a pervasive challenge in Multi-Agent Systems (MAS), which can be represented as a Stackelberg game (SG). However, the scalability of exist…