Summary of Multimodal Auto Validation For Self-refinement in Web Agents, by Ruhana Azam and Tamer Abuelsaad and Aditya Vempaty and Ashish Jagmohan
Multimodal Auto Validation For Self-Refinement in Web Agents
by Ruhana Azam, Tamer Abuelsaad, Aditya Vempaty, Ashish Jagmohan
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces an innovative approach to improve web agent performance through multi-modal validation and self-refinement. Building upon the state-of-the-art Agent-E framework, the researchers explore different modalities (text, vision) and hierarchical structures for automatic validation of web agents. Additionally, a self-refinement mechanism is introduced, utilizing the developed auto-validator to enable web agents to detect and correct workflow failures. The results demonstrate significant gains in task-completion rates, from 76.2% to 81.24%, on the WebVoyager benchmark subset, outperforming the previous state-of-the-art performance of Agent-E. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers that can automate tasks better. It’s like having a virtual assistant that can help you with repetitive jobs. The researchers found a way to make these assistants more reliable by using different ways to understand information (text and images) and improving how they correct mistakes when things go wrong. They tested this new approach on some benchmark data and saw a big improvement in the results, making it possible for computers to do even more complex tasks. |
Keywords
» Artificial intelligence » Multi modal
