Multimodal Auto Validation For Self-Refinement in Web Agents

• URL: http://arxiv.org/abs/2410.00689v2
• Date: Fri, 11 Oct 2024 15:42:52 GMT
• Title: Multimodal Auto Validation For Self-Refinement in Web Agents
• Authors: Ruhana Azam, Tamer Abuelsaad, Aditya Vempaty, Ashish Jagmohan,
• Abstract summary: This paper introduces an approach to improving web agent performance through multi-modal validation and self-refinement. We present a comprehensive study of different modalities (text, vision) and the effect of hierarchy for the automatic validation of web agents. We also introduce a self-refinement mechanism for web automation, using the developed auto-validator, that enables web agents to detect and self-correct workflow failures.
• Score: 0.5843533603338313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As our world digitizes, web agents that can automate complex and monotonous tasks are becoming essential in streamlining workflows. This paper introduces an approach to improving web agent performance through multi-modal validation and self-refinement. We present a comprehensive study of different modalities (text, vision) and the effect of hierarchy for the automatic validation of web agents, building upon the state-of-the-art Agent-E web automation framework. We also introduce a self-refinement mechanism for web automation, using the developed auto-validator, that enables web agents to detect and self-correct workflow failures. Our results show significant gains on Agent-E's (a SOTA web agent) prior state-of-art performance, boosting task-completion rates from 76.2\% to 81.24\% on the subset of the WebVoyager benchmark. The approach presented in this paper paves the way for more reliable digital assistants in complex, real-world scenarios.

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