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AutoWebGLM: A Large Language Model-based Web Navigating Agent

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arxiv 2404.03648 v2 pith:I7GG5VQR submitted 2024-04-04 cs.CL

AutoWebGLM: A Large Language Model-based Web Navigating Agent

Hanyu Lai , Xiao Liu , Iat Long Iong , Shuntian Yao , Yuxuan Chen , Pengbo Shen , Hao Yu , Hanchen Zhang
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Xiaohan Zhang Yuxiao Dong Jie Tang
This is my paper
classification cs.CL
keywords autowebglmnavigationdatatasksagentbrowsinghtmllanguage
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Large language models (LLMs) have fueled many intelligent web agents, but most existing ones perform far from satisfying in real-world web navigation tasks due to three factors: (1) the complexity of HTML text data (2) versatility of actions on webpages, and (3) task difficulty due to the open-domain nature of the web. In light of these challenges, we develop the open AutoWebGLM based on ChatGLM3-6B. AutoWebGLM can serve as a powerful automated web navigation agent that outperform GPT-4. Inspired by human browsing patterns, we first design an HTML simplification algorithm to represent webpages with vital information preserved succinctly. We then employ a hybrid human-AI method to build web browsing data for curriculum training. Finally, we bootstrap the model by reinforcement learning and rejection sampling to further facilitate webpage comprehension, browser operations, and efficient task decomposition by itself. For comprehensive evaluation, we establish a bilingual benchmark -- AutoWebBench -- for real-world web navigation tasks. We evaluate AutoWebGLM across diverse web navigation benchmarks, demonstrating its potential to tackle challenging tasks in real environments. Related code, model, and data are released at \url{https://github.com/THUDM/AutoWebGLM}.

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