  With strong capabilities of reasoning and a broad understanding of the world,
Large Language Models (LLMs) have demonstrated immense potential in building
versatile embodied decision-making agents capable of executing a wide array of
tasks. Nevertheless, when deployed in unfamiliar environments, we show that LLM
agents encounter challenges in efficiently gathering essential information,
leading to suboptimal performance. Conversely, human individuals often seek
additional information from their peers prior to taking action, harnessing
external knowledge to avoid unnecessary trial and error. Drawing inspiration
from this behavior, we propose \textit{Asking Before Acting} (ABA), a method
that empowers the agent to proactively inquire with external sources for
pertinent information using natural language during their interactions within
the environment. In this way, the agent is able to enhance its efficiency and
performance by circumventing potentially laborious steps and combating the
difficulties associated with exploration in unfamiliar environments and
vagueness of the instructions. We conduct extensive experiments involving a
spectrum of environments including text-based household everyday tasks, robot
arm manipulation tasks, and real world open domain image based embodied tasks.
The experiments involve various models from Vicuna to GPT-4. The results
demonstrate that, even with modest prompts modifications, ABA exhibits
substantial advantages on both performance and efficiency over baseline LLM
agents. Further finetuning ABA with reformulated metadata (ABA-FT) faciliates
learning the rationale for asking and allows for additional enhancements
especially in tasks that baselines struggle to solve.
