A Zero-Shot Language Agent for Computer Control with Structured
  Reflection
Large language models (LLMs) have shown increasing capacity at planning and
executing a high-level goal in a live computer environment (e.g. MiniWoB++). To
perform a task, recent works often require a model to learn from trace examples
of the task via either supervised learning or few/many-shot prompting. Without
these trace examples, it remains a challenge how an agent can autonomously
learn and improve its control on a computer, which limits the ability of an
agent to perform a new task. We approach this problem with a zero-shot agent
that requires no given expert traces. Our agent plans for executable actions on
a partially observed environment, and iteratively progresses a task by
identifying and learning from its mistakes via self-reflection and structured
thought management. On the easy tasks of MiniWoB++, we show that our zero-shot
agent often outperforms recent SoTAs, with more efficient reasoning. For tasks
with more complexity, our reflective agent performs on par with prior best
models, even though previous works had the advantages of accessing expert
traces or additional screen information.
652cbbb468f1d7d1d29a24c7
Tao Li
claimed_verified
2023-10-17T07:31:04.350Z
5f8e07f35d083370c711f5d2
https://cdn-avatars.huggingface.co/v1/production/uploads/1603143637927-noauth.jpeg
Tao Li
user
tli8hf
652cbbb468f1d7d1d29a24c8
Gang Li
652cbbb468f1d7d1d29a24c9
Zhiwei Deng
652cbbb468f1d7d1d29a24ca
Bryan Wang
admin_assigned
2024-02-19T15:47:51.793Z
6215a70a6f827861d125fe2e
/avatars/c84b243e02ca2b71af079c8220c5fe44.svg
Bryan Wang
user
bw0514
652cbbb468f1d7d1d29a24cb
Yang Li
A Zero-Shot Language Agent for Computer Control with Structured
  Reflection
Large language models (LLMs) have shown increasing capacity at planning and
executing a high-level goal in a live computer environment (e.g. MiniWoB++). To
perform a task, recent works often require a model to learn from trace examples
of the task via either supervised learning or few/many-shot prompting. Without
these trace examples, it remains a challenge how an agent can autonomously
learn and improve its control on a computer, which limits the ability of an
agent to perform a new task. We approach this problem with a zero-shot agent
that requires no given expert traces. Our agent plans for executable actions on
a partially observed environment, and iteratively progresses a task by
identifying and learning from its mistakes via self-reflection and structured
thought management. On the easy tasks of MiniWoB++, we show that our zero-shot
agent often outperforms recent SoTAs, with more efficient reasoning. For tasks
with more complexity, our reflective agent performs on par with prior best
models, even though previous works had the advantages of accessing expert
traces or additional screen information.
A zero-shot agent learns to perform tasks in a computer environment without expert traces, using self-reflection and structured thought to improve performance, outperforming recent models on easy tasks and matching them on more complex ones.
Large language models zero-shot agent self-reflection structured thought management MiniWoB++