178 citations · 201 across the 13 of their papers we have counts for
5 papers · 1 filter
EcoAct: Economic Agent Determines When to Register What Action
Shaokun Zhang, Jieyu Zhang, Dujian Ding +7
Recent advancements have enabled Large Language Models (LLMs) to function as agents that can perform actions using external tools. This requires registering, i.e., integrating tool…
Adaptive In-conversation Team Building for Language Model Agents
Linxin Song, Jiale Liu, Jieyu Zhang +5
Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks, while the effective design of multiple agents for a particula…
StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows
Yiran Wu, Tianwei Yue, Shaokun Zhang +2
It is a notable trend to use Large Language Models (LLMs) to tackle complex tasks, e.g., tasks that require a sequence of actions and dynamic interaction with tools and external en…
Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications
Negar Arabzadeh, Julia Kiseleva, Qingyun Wu +5
The rapid development in the field of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents to assist humans in their…
Offline Training of Language Model Agents with Functions as Learnable Weights
Shaokun Zhang, Jieyu Zhang, Jiale Liu +4
Researchers and practitioners have recently reframed powerful Large Language Models (LLMs) as agents, enabling them to automate complex tasks largely via the use of specialized fun…