title: Mind2Web: Towards a Generalist Agent for the Web
summary: We introduce Mind2Web, the first dataset for developing and evaluating
  generalist agents for the web that can follow language instructions to complete
  complex tasks on any website. Existing datasets for web agents either use
  simulated websites or only cover a limited set of websites and tasks, thus not
  suitable for generalist web agents. With over 2,000 open-ended tasks collected
  from 137 websites spanning 31 domains and crowdsourced action sequences for the
  tasks, Mind2Web provides three necessary ingredients for building generalist
  web agents: 1) diverse domains, websites, and tasks, 2) use of real-world
  websites instead of simulated and simplified ones, and 3) a broad spectrum of
  user interaction patterns. Based on Mind2Web, we conduct an initial exploration
  of using large language models (LLMs) for building generalist web agents. While
  the raw HTML of real-world websites are often too large to be fed to LLMs, we
  show that first filtering it with a small LM significantly improves the
  effectiveness and efficiency of LLMs. Our solution demonstrates a decent level
  of performance, even on websites or entire domains the model has never seen
  before, but there is still a substantial room to improve towards truly
  generalizable agents. We open-source our dataset, model implementation, and
  trained models (https://osu-nlp-group.github.io/Mind2Web) to facilitate further
  research on building a generalist agent for the web.
paper_authors._id: 648671858e0983eef10d3f31
paper_authors.name: Xiang Deng
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paper_authors.fullname: Xiang Deng
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paper_authors.user: XiangD-OSU
paper_authors._id: 648671858e0983eef10d3f32
paper_authors.name: Yu Gu
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paper_authors.fullname: Yu Gu
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paper_authors.user: entslscheia
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paper_authors.name: Boyuan Zheng
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paper_authors.fullname: Boyuan Zheng
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paper_authors.user: boyuanzheng010
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paper_authors.name: Shijie Chen
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paper_authors.name: Samuel Stevens
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paper_authors.fullname: Samuel Stevens
paper_authors.type: user
paper_authors.user: samuelstevens
paper_authors._id: 648671858e0983eef10d3f36
paper_authors.name: Boshi Wang
paper_authors._id: 648671858e0983eef10d3f37
paper_authors.name: Huan Sun
paper_authors.status: admin_assigned
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paper_authors.fullname: Huan Sun
paper_authors.type: user
paper_authors.user: huansun
paper_authors._id: 648671858e0983eef10d3f38
paper_authors.name: Yu Su
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6477a323dbc2a416f8b852b3/grnmnxO1foz6aRRJ_A2NU.jpeg
paper_authors.fullname: Yu Su
paper_authors.type: user
paper_authors.user: ysu-nlp
paper_title: Mind2Web: Towards a Generalist Agent for the Web
paper_summary: We introduce Mind2Web, the first dataset for developing and evaluating
  generalist agents for the web that can follow language instructions to complete
  complex tasks on any website. Existing datasets for web agents either use
  simulated websites or only cover a limited set of websites and tasks, thus not
  suitable for generalist web agents. With over 2,000 open-ended tasks collected
  from 137 websites spanning 31 domains and crowdsourced action sequences for the
  tasks, Mind2Web provides three necessary ingredients for building generalist
  web agents: 1) diverse domains, websites, and tasks, 2) use of real-world
  websites instead of simulated and simplified ones, and 3) a broad spectrum of
  user interaction patterns. Based on Mind2Web, we conduct an initial exploration
  of using large language models (LLMs) for building generalist web agents. While
  the raw HTML of real-world websites are often too large to be fed to LLMs, we
  show that first filtering it with a small LM significantly improves the
  effectiveness and efficiency of LLMs. Our solution demonstrates a decent level
  of performance, even on websites or entire domains the model has never seen
  before, but there is still a substantial room to improve towards truly
  generalizable agents. We open-source our dataset, model implementation, and
  trained models (https://osu-nlp-group.github.io/Mind2Web) to facilitate further
  research on building a generalist agent for the web.
paper_ai_keywords: Mind2Web generalist agents language instructions web agents simulated websites open-ended tasks crowdsourced action sequences real-world websites large language models (LLMs) raw HTML small LM effectiveness efficiency true generalization