title: LASER: LLM Agent with State-Space Exploration for Web Navigation
summary: Large language models (LLMs) have been successfully adapted for interactive
  decision-making tasks like web navigation. While achieving decent performance,
  previous methods implicitly assume a forward-only execution mode for the model,
  where they only provide oracle trajectories as in-context examples to teach the
  model how to reason in the interactive environment. Consequently, the model
  could not handle more challenging scenarios not covered in the in-context
  examples, e.g., mistakes, leading to sub-optimal performance. To address this
  issue, we propose to model the interactive task as state space exploration,
  where the LLM agent transitions among a pre-defined set of states by performing
  actions to complete the task. This formulation enables flexible back-tracking,
  allowing the model to easily recover from errors. We evaluate our proposed LLM
  Agent with State-Space ExploRation (LASER) on the WebShop task. Experimental
  results show that our LASER agent significantly outperforms previous methods
  and closes the gap with human performance on the web navigation task.
paper_authors._id: 6507a79cd55dd4e15cc79952
paper_authors.name: Kaixin Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-18T10:19:26.368Z
paper_authors._id: 64ae4f6280f308a395fd7c19
paper_authors.avatarUrl: /avatars/5f1330f8187cd5e66aa517303659f110.svg
paper_authors.fullname: Kaixin Ma
paper_authors.type: user
paper_authors.user: kaixinm
paper_authors._id: 6507a79cd55dd4e15cc79953
paper_authors.name: Hongming Zhang
paper_authors._id: 6507a79cd55dd4e15cc79954
paper_authors.name: Hongwei Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-18T10:20:42.554Z
paper_authors._id: 6467671376bb704aa1413cf9
paper_authors.avatarUrl: /avatars/78fd7e43ee1705db55540a19266568ec.svg
paper_authors.fullname: wanghongwei
paper_authors.type: user
paper_authors.user: Hongwei2023
paper_authors._id: 6507a79cd55dd4e15cc79955
paper_authors.name: Xiaoman Pan
paper_authors._id: 6507a79cd55dd4e15cc79956
paper_authors.name: Dong Yu
paper_title: LASER: LLM Agent with State-Space Exploration for Web Navigation
paper_summary: Large language models (LLMs) have been successfully adapted for interactive
  decision-making tasks like web navigation. While achieving decent performance,
  previous methods implicitly assume a forward-only execution mode for the model,
  where they only provide oracle trajectories as in-context examples to teach the
  model how to reason in the interactive environment. Consequently, the model
  could not handle more challenging scenarios not covered in the in-context
  examples, e.g., mistakes, leading to sub-optimal performance. To address this
  issue, we propose to model the interactive task as state space exploration,
  where the LLM agent transitions among a pre-defined set of states by performing
  actions to complete the task. This formulation enables flexible back-tracking,
  allowing the model to easily recover from errors. We evaluate our proposed LLM
  Agent with State-Space ExploRation (LASER) on the WebShop task. Experimental
  results show that our LASER agent significantly outperforms previous methods
  and closes the gap with human performance on the web navigation task.
paper_ai_keywords: large language models (LLMs) interactive decision-making tasks web navigation forward-only execution mode oracle trajectories in-context examples reasoning in the interactive environment state space exploration LLM agent pre-defined set of states actions task completion flexible back-tracking error recovery LLM Agent with State-Space ExploRation (LASER) WebShop task