[1B5-OS-41c] OS-41

[1B5-OS-41c-01] Web Agent with Meta-Prompt-Driven Expert Integration.

〇Satoi Yamaguchi¹, Yuna Mastunaga², Takayasu Ikeda³, Masahiro Suzuki⁴, Yutaka Matuso⁴ (1. Waseda University, 2. Makuhari Senior High School, 3. Advanced Institute of Industrial Technology, 4. Graduate School of Engineering, The University of Tokyo)

Keywords:Web agent, LLM, meta prompt

This research explores a novel approach to task completion that does not rely on extensive pre-training in the rapidly evolving field of Web Agents. A major challenge existing Agents face is the automation of tasks accompanying image recognition. However, previous methods highlight limited compatibility between image recognition performance and approaches that do not require pre-training. To address this limitation, we propose an approach that strategically integrates several expert methods employing meta-prompt within LLM, achieving advanced environmental analysis that enables performance improvement. We evaluate the proposed approach using MiniWob++. Additionally, we compare existing Agents to the proposed approach to access task success rate. This paper offers insight into the potential of integration of meta-prompt using LLM to improve task completion rate, suggesting the possibility of a decrease in the necessity of extensive data collection and training required by current agents.