Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators

TL;DR

This paper introduces Responsible Task Automation (ResponsibleTA), a framework that enables large language models to collaborate responsibly in automating tasks by predicting feasibility, verifying completeness, and enhancing security, especially in UI automation.

Contribution

The paper proposes a novel ResponsibleTA framework with three capabilities for responsible LLM-based task automation, including feasibility prediction, completeness verification, and security enhancement.

Findings

ResponsibleTA improves task automation safety and reliability.

Two paradigms for feasibility and completeness prediction are compared.

Local memory mechanism enhances security and privacy protection.

Abstract

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.