LASER: LLM Agent with State-Space Exploration for Web Navigation
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.
6507a79cd55dd4e15cc79952
Kaixin Ma
admin_assigned
2023-09-18T10:19:26.368Z
64ae4f6280f308a395fd7c19
/avatars/5f1330f8187cd5e66aa517303659f110.svg
Kaixin Ma
user
kaixinm
6507a79cd55dd4e15cc79953
Hongming Zhang
6507a79cd55dd4e15cc79954
Hongwei Wang
admin_assigned
2023-09-18T10:20:42.554Z
6467671376bb704aa1413cf9
/avatars/78fd7e43ee1705db55540a19266568ec.svg
wanghongwei
user
Hongwei2023
6507a79cd55dd4e15cc79955
Xiaoman Pan
6507a79cd55dd4e15cc79956
Dong Yu
LASER: LLM Agent with State-Space Exploration for Web Navigation
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.
The proposed LASER agent improves LLM performance in interactive decision-making tasks by modeling them as state space exploration, enabling flexible back-tracking and better error recovery.
large language models LLM interactive decision-making web navigation oracle trajectories in-context examples state space exploration action task completion flexible back-tracking webshop task human performance