title: A Real-World WebAgent with Planning, Long Context Understanding, and
    Program Synthesis
summary: Pre-trained large language models (LLMs) have recently achieved better
  generalization and sample efficiency in autonomous web navigation. However, the
  performance on real-world websites has still suffered from (1) open domainness,
  (2) limited context length, and (3) lack of inductive bias on HTML. We
  introduce WebAgent, an LLM-driven agent that can complete the tasks on real
  websites following natural language instructions. WebAgent plans ahead by
  decomposing instructions into canonical sub-instructions, summarizes long HTML
  documents into task-relevant snippets, and acts on websites via generated
  Python programs from those. We design WebAgent with Flan-U-PaLM, for grounded
  code generation, and HTML-T5, new pre-trained LLMs for long HTML documents
  using local and global attention mechanisms and a mixture of long-span
  denoising objectives, for planning and summarization. We empirically
  demonstrate that our recipe improves the success on a real website by over 50%,
  and that HTML-T5 is the best model to solve HTML-based tasks; achieving 14.9%
  higher success rate than prior SoTA on the MiniWoB web navigation benchmark and
  better accuracy on offline task planning evaluation.
paper_authors._id: 64bf412046cc3cdfbb29e894
paper_authors.name: Izzeddin Gur
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-26T08:44:06.313Z
paper_authors._id: 64c0ccc19832a455bc2a2069
paper_authors.avatarUrl: /avatars/9d4fdf13cd62c6c258b5f50d67c8a1b1.svg
paper_authors.fullname: IZZEDDIN GUR
paper_authors.type: user
paper_authors.user: izzeddingur
paper_authors._id: 64bf412046cc3cdfbb29e895
paper_authors.name: Hiroki Furuta
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-25T08:14:29.475Z
paper_authors._id: 646c39fc3e2a7b06594bac6c
paper_authors.avatarUrl: /avatars/8925fef2b6e7105a0f0a4bbf160cfda3.svg
paper_authors.fullname: Hiroki Furuta
paper_authors.type: user
paper_authors.user: frt03
paper_authors._id: 64bf412046cc3cdfbb29e896
paper_authors.name: Austin Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-25T08:15:21.796Z
paper_authors._id: 6020b14c8345068224043bc0
paper_authors.avatarUrl: /avatars/1c5c02f9165e188ab81a53efabc062e2.svg
paper_authors.fullname: Austin Huang
paper_authors.type: user
paper_authors.user: austinvhuang
paper_authors._id: 64bf412046cc3cdfbb29e897
paper_authors.name: Mustafa Safdari
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-25T08:15:49.843Z
paper_authors._id: 6430b08b74e7df5793edf353
paper_authors.avatarUrl: /avatars/67875a201ffc22416acbf74b759b544b.svg
paper_authors.fullname: Mustafa Safdari
paper_authors.type: user
paper_authors.user: TheMegusAlexandrus
paper_authors._id: 64bf412046cc3cdfbb29e898
paper_authors.name: Yutaka Matsuo
paper_authors._id: 64bf412046cc3cdfbb29e899
paper_authors.name: Douglas Eck
paper_authors._id: 64bf412046cc3cdfbb29e89a
paper_authors.name: Aleksandra Faust
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-25T08:17:22.594Z
paper_authors._id: 64a3b828225b2121f6b3beb3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64a3b828225b2121f6b3beb3/44AgAQENc7cJMOWcoK4g_.jpeg
paper_authors.fullname: Aleksandra Faust
paper_authors.type: user
paper_authors.user: sandraorion
paper_title: A Real-World WebAgent with Planning, Long Context Understanding, and
    Program Synthesis
paper_summary: Pre-trained large language models (LLMs) have recently achieved better
  generalization and sample efficiency in autonomous web navigation. However, the
  performance on real-world websites has still suffered from (1) open domainness,
  (2) limited context length, and (3) lack of inductive bias on HTML. We
  introduce WebAgent, an LLM-driven agent that can complete the tasks on real
  websites following natural language instructions. WebAgent plans ahead by
  decomposing instructions into canonical sub-instructions, summarizes long HTML
  documents into task-relevant snippets, and acts on websites via generated
  Python programs from those. We design WebAgent with Flan-U-PaLM, for grounded
  code generation, and HTML-T5, new pre-trained LLMs for long HTML documents
  using local and global attention mechanisms and a mixture of long-span
  denoising objectives, for planning and summarization. We empirically
  demonstrate that our recipe improves the success on a real website by over 50%,
  and that HTML-T5 is the best model to solve HTML-based tasks; achieving 14.9%
  higher success rate than prior SoTA on the MiniWoB web navigation benchmark and
  better accuracy on offline task planning evaluation.
paper_ai_keywords: LLMs large language models autonomous web navigation open domainness context length inductive bias HTML canonical sub-instructions task-relevant snippets Python programs Flan-U-PaLM HTML-T5 LLM-driven agent local attention mechanisms global attention mechanisms long-span denoising objectives grounded achieving MiniWoB web navigation benchmark offline task planning evaluation