title: Responsible Task Automation: Empowering Large Language Models as
    Responsible Task Automators
summary: 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.
paper_authors._id: 647d5242d2da33779cbd8273
paper_authors.name: Zhizheng Zhang
paper_authors._id: 647d5242d2da33779cbd8274
paper_authors.name: Xiaoyi Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-02T11:51:55.425Z
paper_authors._id: 6582b79aafc6b50a2cbaa5c8
paper_authors.avatarUrl: /avatars/65fa21ca144177f232347084b0e057c5.svg
paper_authors.fullname: Xiaoyi Zhang
paper_authors.type: user
paper_authors.user: xyzhang626
paper_authors._id: 647d5242d2da33779cbd8275
paper_authors.name: Wenxuan Xie
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-06T08:40:17.478Z
paper_authors._id: 647e80910ed7d0c8760e2afc
paper_authors.avatarUrl: /avatars/8cd85c19c5789cd500a75ba6f72ab6e5.svg
paper_authors.fullname: Wenxuan Xie
paper_authors.type: user
paper_authors.user: wenxuanxie
paper_authors._id: 647d5242d2da33779cbd8276
paper_authors.name: Yan Lu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-06T08:40:14.201Z
paper_authors._id: 647e85231a1fcad2fdc4edf3
paper_authors.avatarUrl: /avatars/aa5bf71f9405bd2f7b96df17d56684b0.svg
paper_authors.fullname: Yan Lu
paper_authors.type: user
paper_authors.user: csyanlu
paper_title: Responsible Task Automation: Empowering Large Language Models as
    Responsible Task Automators
paper_summary: 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.
paper_ai_summary: Responsible Task Automation (ResponsibleTA) leverages LLMs to ensure responsible collaboration between coordinators and executors in task automation by assessing feasibility, completeness, and security.
paper_ai_keywords: Large Language Models Responsible Task Automation ResponsibleTA prompt engineering domain-specific learnable models local memory mechanism UI task automation