負責任任務自動化：賦予大型語言模型作為負責任任務自動化者的能力

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

June 2, 2023

作者: Zhizheng Zhang, Xiaoyi Zhang, Wenxuan Xie, Yan Lu

cs.AI

摘要

大型語言模型（LLMs）最近取得的成功代表著人工通用智能邁出了令人印象深刻的一步。它們展示了在用戶指令下自動完成任務的潛力，充當類似大腦的協調者。隨著我們將越來越多的任務委派給機器進行自動完成，相應的風險將會浮出水面。一個重要問題浮現：當幫助人類自動化任務作為個人副駕駛員時，我們如何使機器表現出負責任的行為？在本文中，我們從可行性、完整性和安全性的角度深入探討這個問題。具體而言，我們提出了負責任任務自動化（ResponsibleTA）作為一個基本框架，以促進基於LLM協調者和執行者之間負責任的任務自動化合作，具備三項增強功能：1）預測執行者命令的可行性；2）驗證執行者的完整性；3）增強安全性（例如，保護用戶隱私）。我們進一步提出並比較了實現前兩項功能的兩種範式。一種是通過提示工程利用LLMs本身的通用知識，另一種是採用特定領域的可學習模型。此外，我們引入了一種本地記憶機制來實現第三項功能。我們在UI任務自動化上評估了我們提出的ResponsibleTA，並希望它能引起更多關注，確保LLMs在各種場景中更加負責任。研究項目主頁位於https://task-automation-research.github.io/responsible_task_automation。

English

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.