With strong capabilities of reasoning and a generic understanding of the
world, Large Language Models (LLMs) have shown great potential in building
versatile embodied decision making agents capable of performing diverse tasks.
However, when deployed to unfamiliar environments, we show that LLM agents face
challenges in efficiently gathering necessary information, leading to
suboptimal performance. On the other hand, in unfamiliar scenarios, human
individuals often seek additional information from their peers before taking
action, leveraging external knowledge to avoid unnecessary trial and error.
Building upon this intuition, we propose \textit{Asking Before Action} (ABA), a
method that empowers the agent to proactively query external sources for
pertinent information using natural language during their interactions in the
environment. In this way, the agent is able to enhance its efficiency and
performance by mitigating wasteful steps and circumventing the difficulties
associated with exploration in unfamiliar environments. We empirically evaluate
our method on an embodied decision making benchmark, ALFWorld, and demonstrate
that despite modest modifications in prompts, our method exceeds baseline LLM
agents by more than $40$%. Further experiments on two variants of ALFWorld
illustrate that by imitation learning, ABA effectively retains and reuses
queried and known information in subsequent tasks, mitigating the need for
repetitive inquiries. Both qualitative and quantitative results exhibit
remarkable performance on tasks that previous methods struggle to solve.

提出一种名为 “行动前询问”（Asking Before Action，ABA）的方法，利用自然语言引导大型语言模型（LLMs）在未知环境中主动查询外部知识，从而增强其效率和性能，实验证明该方法在 ALFWorld 等体验决策基准中表现出更好的性能。