Responsible Task Automation: Empowering Large Language Models as
  Responsible Task Automators
The recent success of Large Language Models (LLMs) signifies an impressive
stride towards artificial general intelligence. They have shown a promising
prospect in automatically completing tasks upon user instructions, functioning
as brain-like coordinators. The associated risks will be revealed as we
delegate an increasing number of tasks to machines for automated completion. A
big question emerges: how can we make machines behave responsibly when helping
humans automate tasks as personal copilots? In this paper, we explore this
question in depth from the perspectives of feasibility, completeness and
security. In specific, we present Responsible Task Automation (ResponsibleTA)
as a fundamental framework to facilitate responsible collaboration between
LLM-based coordinators and executors for task automation with three empowered
capabilities: 1) predicting the feasibility of the commands for executors; 2)
verifying the completeness of executors; 3) enhancing the security (e.g., the
protection of users' privacy). We further propose and compare two paradigms for
implementing the first two capabilities. One is to leverage the generic
knowledge of LLMs themselves via prompt engineering while the other is to adopt
domain-specific learnable models. Moreover, we introduce a local memory
mechanism for achieving the third capability. We evaluate our proposed
ResponsibleTA on UI task automation and hope it could bring more attentions to
ensuring LLMs more responsible in diverse scenarios. The research project
homepage is at
https://task-automation-research.github.io/responsible_task_automation.
647d5242d2da33779cbd8273
Zhizheng Zhang
647d5242d2da33779cbd8274
Xiaoyi Zhang
claimed_verified
2024-04-02T11:51:55.425Z
6582b79aafc6b50a2cbaa5c8
/avatars/65fa21ca144177f232347084b0e057c5.svg
Xiaoyi Zhang
user
xyzhang626
647d5242d2da33779cbd8275
Wenxuan Xie
claimed_verified
2023-06-06T08:40:17.478Z
647e80910ed7d0c8760e2afc
/avatars/8cd85c19c5789cd500a75ba6f72ab6e5.svg
Wenxuan Xie
user
wenxuanxie
647d5242d2da33779cbd8276
Yan Lu
claimed_verified
2023-06-06T08:40:14.201Z
647e85231a1fcad2fdc4edf3
/avatars/aa5bf71f9405bd2f7b96df17d56684b0.svg
Yan Lu
user
csyanlu
Responsible Task Automation: Empowering Large Language Models as
  Responsible Task Automators
The recent success of Large Language Models (LLMs) signifies an impressive
stride towards artificial general intelligence. They have shown a promising
prospect in automatically completing tasks upon user instructions, functioning
as brain-like coordinators. The associated risks will be revealed as we
delegate an increasing number of tasks to machines for automated completion. A
big question emerges: how can we make machines behave responsibly when helping
humans automate tasks as personal copilots? In this paper, we explore this
question in depth from the perspectives of feasibility, completeness and
security. In specific, we present Responsible Task Automation (ResponsibleTA)
as a fundamental framework to facilitate responsible collaboration between
LLM-based coordinators and executors for task automation with three empowered
capabilities: 1) predicting the feasibility of the commands for executors; 2)
verifying the completeness of executors; 3) enhancing the security (e.g., the
protection of users' privacy). We further propose and compare two paradigms for
implementing the first two capabilities. One is to leverage the generic
knowledge of LLMs themselves via prompt engineering while the other is to adopt
domain-specific learnable models. Moreover, we introduce a local memory
mechanism for achieving the third capability. We evaluate our proposed
ResponsibleTA on UI task automation and hope it could bring more attentions to
ensuring LLMs more responsible in diverse scenarios. The research project
homepage is at
https://task-automation-research.github.io/responsible_task_automation.
Responsible Task Automation (ResponsibleTA) leverages LLMs to ensure responsible collaboration between coordinators and executors in task automation by assessing feasibility, completeness, and security.
Large Language Models Responsible Task Automation ResponsibleTA prompt engineering domain-specific learnable models local memory mechanism UI task automation