AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web
  Navigating Agent
Large language models (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 https://github.com/THUDM/AutoWebGLM.
660f6455dcdd0ff2df12f559
Hanyu Lai
660f6455dcdd0ff2df12f55a
Xiao Liu
claimed_verified
2024-11-05T16:12:35.192Z
62f098602ca4d32a7cd87aba
https://cdn-avatars.huggingface.co/v1/production/uploads/1659934844914-62f098602ca4d32a7cd87aba.jpeg
Xiao Liu
user
ShawLiu
660f6455dcdd0ff2df12f55b
Iat Long Iong
660f6455dcdd0ff2df12f55c
Shuntian Yao
660f6455dcdd0ff2df12f55d
Yuxuan Chen
660f6455dcdd0ff2df12f55e
Pengbo Shen
660f6455dcdd0ff2df12f55f
Hao Yu
660f6455dcdd0ff2df12f560
Hanchen Zhang
660f6455dcdd0ff2df12f561
Xiaohan Zhang
660f6455dcdd0ff2df12f562
Yuxiao Dong
660f6455dcdd0ff2df12f563
Jie Tang
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web
  Navigating Agent
Large language models (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 https://github.com/THUDM/AutoWebGLM.
AutoWebGLM, an automated web navigation agent based on ChatGLM3-6B, addresses webpage complexity and action versatility through HTML simplification and reinforcement learning, outperforming GPT-4 in real-world web navigation tasks.
LLMs GPT-4 ChatGLM3-6B HTML simplification reinforcement learning rejection sampling curriculum training bilingual benchmark AutoWebBench