压缩网页历史状态提升自动化精度，效果显著。
Turbocharging Web Automation: The Impact of Compressed History States
- 用固定长度向量压缩历史页面状态，提取关键信息。
- 在两个数据集上比无历史状态基线提升1.2%-5.4%准确率。
- 适合需要高效利用历史信息的自动化系统开发者。
语言模型推动了网页自动化的发展。现有方法虽结合当前网页状态、历史动作和自然语言指令预测下一步操作，却忽视了历史状态的重要性。然而，网页状态高度冗长，导致输入序列过长且信息稀疏，难以有效利用历史状态。本文提出一种新型网页历史压缩方法，通过历史压缩模块将每个历史状态中最相关的任务信息提炼为固定长度的短表示，缓解冗长状态带来的挑战。在Mind2Web和WebLINX数据集上的实验表明，该方法相比不使用历史状态的基线，准确率提升1.2%至5.4%绝对值。
原文摘要 · Abstract (English)
Language models have led to a leap forward in web automation. The current web automation approaches take the current web state, history actions, and language instruction as inputs to predict the next action, overlooking the importance of history states. However, the highly verbose nature of web page states can result in long input sequences and sparse information, hampering the effective utilization of history states. In this paper, we propose a novel web history compressor approach to turbocharge web automation using history states. Our approach employs a history compressor module that distills the most task-relevant information from each history state into a fixed-length short representation, mitigating the challenges posed by the highly verbose history states. Experiments are conducted on the Mind2Web and WebLINX datasets to evaluate the effectiveness of our approach. Results show that our approach obtains 1.2-5.4% absolute accuracy improvements compared to the baseline approach without history inputs.
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