E-ANT: A Large-Scale Dataset for Efficient Automatic GUI NavigaTion

TL;DR

This paper introduces E-ANT, a large-scale Chinese GUI navigation dataset with real human traces and high-quality screenshots, aimed at advancing multimodal large language models' ability to perform efficient mobile GUI navigation.

Contribution

The paper presents E-ANT, the first Chinese GUI navigation dataset with real human behavior data, high-quality annotations, and extensive coverage of over 5000 tinyAPPs, facilitating improved model training and evaluation.

Findings

MLLMs show promising performance on E-ANT

The dataset enables effective evaluation of GUI navigation models

Abalation studies highlight key factors influencing model accuracy

Abstract

Online GUI navigation on mobile devices has driven a lot of attention recent years since it contributes to many real-world applications. With the rapid development of large language models (LLM), multimodal large language models (MLLM) have tremendous potential on this task. However, existing MLLMs need high quality data to improve its abilities of making the correct navigation decisions according to the human user inputs. In this paper, we developed a novel and highly valuable dataset, named \textbf{E-ANT}, as the first Chinese GUI navigation dataset that contains real human behaviour and high quality screenshots with annotations, containing nearly 40,000 real human traces over 5000+ different tinyAPPs. Furthermore, we evaluate various powerful MLLMs on E-ANT and show their experiments results with sufficient ablations. We believe that our proposed dataset will be beneficial for both the evaluation and development of GUI navigation and LLM/MLLM decision-making capabilities.