Augmenting large language models (LLM) to use external tools enhances their
performance across a variety of tasks. However, prior works over-rely on
task-specific demonstration of tool use that limits their generalizability and
computational cost due to making many calls to large-scale LLMs. We introduce
GEAR, a computationally efficient query-tool grounding algorithm that is
generalizable to various tasks that require tool use while not relying on
task-specific demonstrations. GEAR achieves better efficiency by delegating
tool grounding and execution to small language models (SLM) and LLM,
respectively; while leveraging semantic and pattern-based evaluation at both
question and answer levels for generalizable tool grounding. We evaluate GEAR
on 14 datasets across 6 downstream tasks, demonstrating its strong
generalizability to novel tasks, tools and different SLMs. Despite offering
more efficiency, GEAR achieves higher precision in tool grounding compared to
prior strategies using LLM prompting, thus improving downstream accuracy at a
reduced computational cost. For example, we demonstrate that GEAR-augmented
GPT-J and GPT-3 outperform counterpart tool-augmented baselines because of
better tool use.

GEAR 是一种计算高效的查询工具接地算法，能够在不依赖任务特定演示的情况下，提供任务特定工具的使用，并且具有更高的计算效率和较高的工具接地精确性。