AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
(2404.03648)Abstract
LLMs have fueled many intelligent agent tasks, such as web navigation -- but most existing agents perform far from satisfying in real-world webpages due to three factors: (1) the versatility of actions on webpages, (2) HTML text exceeding model processing capacity, and (3) the complexity of decision-making due to the open-domain nature of web. In light of the challenge, we develop AutoWebGLM, a GPT-4-outperforming automated web navigation agent built upon ChatGLM3-6B. Inspired by human browsing patterns, we design an HTML simplification algorithm to represent webpages, preserving vital information succinctly. We employ a hybrid human-AI method to build web browsing data for curriculum training. Then, we bootstrap the model by reinforcement learning and rejection sampling to further facilitate webpage comprehension, browser operations, and efficient task decomposition by itself. For testing, we establish a bilingual benchmark -- AutoWebBench -- for real-world web browsing tasks. We evaluate AutoWebGLM across diverse web navigation benchmarks, revealing its improvements but also underlying challenges to tackle real environments. Related code, model, and data will be released at \url{https://github.com/THUDM/AutoWebGLM}.
Overview
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AutoWebGLM introduces a ground-breaking advancement in AI for web navigation, employing the ChatGLM3-6B model to outperform previous benchmarks like GPT-4.
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It addresses the challenges of web navigation through unified action spaces, HTML simplification, and high-quality training trajectory generation.
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Methodological innovations include HTML representation, hybrid human-AI data construction, and sequential training strategies to enhance model performance.
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Empirical evaluations on the AutoWebBench benchmark showcase AutoWebGLM's superior performance, setting a new standard in AI-powered web navigating agent development.
AutoWebGLM: Innovations and Evaluations in AI-Powered Web Navigation Agents
Introduction to AutoWebGLM
The development of AutoWebGLM introduces a significant enhancement in web navigation agent capabilities, employing the ChatGLM3-6B model as its backbone. This agent surpasses previous benchmarks, including GPT-4, in automated web navigation by embracing a tailored approach to webpage understanding and interaction. The model's unique contributions lie in its sophisticated handling of the complexities associated with web navigation, including diverse action spaces, HTML simplification for efficient processing, and the generation of high-quality training trajectories.
Challenges Addressed
AutoWebGLM's design directly confronts the primary hurdles in web navigation automation:
- Unified Action Space: It establishes a comprehensive action space that enables seamless interactions across a plethora of websites.
- HTML Simplification: By implementing an algorithm that condenses HTML content while preserving essential information, AutoWebGLM ensures the model's operability under the constraint of token length.
- High-quality Training Trajectories: Through a combination of model-assisted and manual annotation methods, AutoWebGLM generates a dataset conducive to training robust web navigating agents capable of accurate inference and error correction.
Methodological Insights
The foundation of AutoWebGLM lies in its methodological innovations:
- HTML Representation: The system incorporates an HTML simplification algorithm inspired by human web browsing patterns, significantly reducing the complexity and verbosity of webpages for model comprehension.
- Hybrid Human-AI Data Construction: This approach enables the rapid assembly of a rich dataset that the model uses for training, refining its understanding of web operations and decisions.
- Curriculum Learning and Reinforcement Approaches: Sequential training strategies involving curriculum learning, reinforcement learning (RL), and rejection sampling finetuning (RFT) are employed to progressively enhance the model's performance across various stages of web interaction.
Dataset and Benchmark Development
A notable contribution of AutoWebGLM's development is the construction of AutoWebBench, a bilingual (English and Chinese) benchmark that addresses the need for comprehensive evaluation tools in web navigation research. This benchmark is designed to assess an agent's performance in navigating and interacting with real-world webpages, offering insights into the practical applicability of AI-powered web agents.
Empirical Evaluations and Findings
Extensive testing of AutoWebGLM across multiple benchmarks, including the newly developed AutoWebBench, reveals its superior performance compared to existing LLM-based web navigating agents. The model demonstrates not only significant improvements in various web navigation tasks but also highlights areas for further research and development.
- Performance Metrics: AutoWebGLM achieves high success rates across diverse web navigation benchmarks, showcasing its robustness and versatility.
- Challenges in Real-World Navigation: Despite its achievements, AutoWebGLM's performance also underlines the complexity of real-world web navigation and the need for ongoing enhancements in model training and environmental understanding.
Concluding Remarks
The introduction of AutoWebGLM marks a pivotal advancement in the field of AI-powered web navigation. By addressing fundamental challenges and integrating innovative training methodologies, AutoWebGLM sets a new standard for the development of intelligent web navigating agents. The AutoWebBench benchmark further enriches research resources, paving the way for future innovations in AI-driven web interactions. As web navigation continues to evolve, AutoWebGLM represents a significant step forward in harnessing the potential of LLMs to navigate the vast expanse of the internet effectively.
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