We create GTA (a benchmark forGeneral Tool Agents) to evaluate the general tool-use ability ofLLMs inreal-worldscenarios. Who created the dataset (e.g., which team, research group) and on behalf of which entity(e.g.,company,institution,organization)?

Our approach employs a sequence of visual resamplers that capture visual details at various spacial scales.

Current deep networks are very data-hungry and benefit from training on large-scale datasets, which are often time-consuming to collect and annotate.