Mobile-Env: Building Qualified Evaluation Benchmarks for LLM-GUI Interaction

• URL: http://arxiv.org/abs/2305.08144v4
• Date: Thu, 13 Jun 2024 11:51:37 GMT
• Title: Mobile-Env: Building Qualified Evaluation Benchmarks for LLM-GUI Interaction
• Authors: Danyang Zhang, Zhennan Shen, Rui Xie, Situo Zhang, Tianbao Xie, Zihan Zhao, Siyuan Chen, Lu Chen, Hongshen Xu, Ruisheng Cao, Kai Yu,
• Abstract summary: We introduce Mobile-Env, a comprehensive toolkit tailored for creating GUI benchmarks in the Android mobile environment. We collect an open-world task set across various real-world apps and a fixed world set, WikiHow, which captures a significant amount of dynamic online contents. Our findings reveal that even advanced models struggle with tasks that are relatively simple for humans.
• Score: 28.53259866617677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Graphical User Interface (GUI) is pivotal for human interaction with the digital world, enabling efficient device control and the completion of complex tasks. Recent progress in Large Language Models (LLMs) and Vision Language Models (VLMs) offers the chance to create advanced GUI agents. To ensure their effectiveness, there's a pressing need for qualified benchmarks that provide trustworthy and reproducible evaluations -- a challenge current benchmarks often fail to address. To tackle this issue, we introduce Mobile-Env, a comprehensive toolkit tailored for creating GUI benchmarks in the Android mobile environment. Mobile-Env offers an isolated and controllable setting for reliable evaluations, and accommodates intermediate instructions and rewards to reflect real-world usage more naturally. Utilizing Mobile-Env, we collect an open-world task set across various real-world apps and a fixed world set, WikiHow, which captures a significant amount of dynamic online contents for fully controllable and reproducible evaluation. We conduct comprehensive evaluations of LLM agents using these benchmarks. Our findings reveal that even advanced models (e.g., GPT-4V and LLaMA-3) struggle with tasks that are relatively simple for humans. This highlights a crucial gap in current models and underscores the importance of developing more capable foundation models and more effective GUI agent frameworks.

Related papers

• AppVLM: A Lightweight Vision Language Model for Online App Control [39.91330570886891]
  We introduce AppVLM, a lightweight Vision-Language Model (VLM) First, we fine-tune it offline on the AndroidControl dataset. Then, we refine its policy by collecting data from the AndroidWorld environment.
  arXiv  Detail & Related papers  (2025-02-10T12:32:21Z)
• The BrowserGym Ecosystem for Web Agent Research [151.90034093362343]
  BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents. We conduct the first large-scale, multi-benchmark web agent experiment. Results highlight a large discrepancy between OpenAI and Anthropic's latests models.
  arXiv  Detail & Related papers  (2024-12-06T23:43:59Z)
• Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction [69.57190742976091]
  We introduce Aguvis, a unified vision-based framework for autonomous GUI agents. Our approach leverages image-based observations, and grounding instructions in natural language to visual elements. To address the limitations of previous work, we integrate explicit planning and reasoning within the model.
  arXiv  Detail & Related papers  (2024-12-05T18:58:26Z)
• GUI Agents with Foundation Models: A Comprehensive Survey [91.97447457550703]
  This survey consolidates recent research on (M)LLM-based GUI agents. We identify key challenges and propose future research directions. We hope this survey will inspire further advancements in the field of (M)LLM-based GUI agents.
  arXiv  Detail & Related papers  (2024-11-07T17:28:10Z)
• AutoBench-V: Can Large Vision-Language Models Benchmark Themselves? [65.92331309449015]
  We introduce AutoBench-V, an automated framework for serving evaluation on demand, i.e., benchmarking LVLMs based on specific aspects of model capability. Through an extensive evaluation of nine popular LVLMs across five demanded user inputs, the framework shows effectiveness and reliability.
  arXiv  Detail & Related papers  (2024-10-28T17:55:08Z)
• EDGE: Enhanced Grounded GUI Understanding with Enriched Multi-Granularity Synthetic Data [15.801018643716437]
  This paper aims to enhance the GUI understanding and interacting capabilities of large vision-language models (LVLMs) through a data-driven approach. We propose EDGE, a general data synthesis framework that automatically generates large-scale, multi-granularity training data from webpages across the Web. Our approach significantly reduces the dependence on manual annotations, empowering researchers to harness the vast public resources available on the Web to advance their work.
  arXiv  Detail & Related papers  (2024-10-25T10:46:17Z)
• SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation [89.24729958546168]
  We present SPA-Bench, a comprehensive SmartPhone Agent Benchmark designed to evaluate (M)LLM-based agents. SPA-Bench offers three key contributions: A diverse set of tasks covering system and third-party apps in both English and Chinese, focusing on features commonly used in daily routines. A novel evaluation pipeline that automatically assesses agent performance across multiple dimensions, encompassing seven metrics related to task completion and resource consumption.
  arXiv  Detail & Related papers  (2024-10-19T17:28:48Z)
• Benchmarking Mobile Device Control Agents across Diverse Configurations [19.01954948183538]
  B-MoCA is a benchmark for evaluating and developing mobile device control agents. We benchmark diverse agents, including agents employing large language models (LLMs) or multi-modal LLMs. While these agents demonstrate proficiency in executing straightforward tasks, their poor performance on complex tasks highlights significant opportunities for future research to improve effectiveness.
  arXiv  Detail & Related papers  (2024-04-25T14:56:32Z)
• ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automation [30.693616802332745]
  This paper presents a novel benchmark, AssistGUI, to evaluate whether models are capable of manipulating the mouse and keyboard on the Windows platform in response to user-requested tasks. We propose an advanced Actor-Critic framework, which incorporates a sophisticated GUI driven by an AI agent and adept at handling lengthy procedural tasks.
  arXiv  Detail & Related papers  (2023-12-20T15:28:38Z)
• TaskBench: Benchmarking Large Language Models for Task Automation [82.2932794189585]
  We introduce TaskBench, a framework to evaluate the capability of large language models (LLMs) in task automation. Specifically, task decomposition, tool selection, and parameter prediction are assessed. Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation.
  arXiv  Detail & Related papers  (2023-11-30T18:02:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.