  Structured knowledge grounding (SKG) leverages structured knowledge to
complete user requests, such as semantic parsing over databases and question
answering over knowledge bases. Since the inputs and outputs of SKG tasks are
heterogeneous, they have been studied separately by different communities,
which limits systematic and compatible research on SKG. In this paper, we
overcome this limitation by proposing the UnifiedSKG framework, which unifies
21 SKG tasks into a text-to-text format, aiming to promote systematic SKG
research, instead of being exclusive to a single task, domain, or dataset. We
use UnifiedSKG to benchmark T5 with different sizes and show that T5, with
simple modifications when necessary, achieves state-of-the-art performance on
almost all of the 21 tasks. We further demonstrate that multi-task
prefix-tuning improves the performance on most tasks, largely improving the
overall performance. UnifiedSKG also facilitates the investigation of zero-shot
and few-shot learning, and we show that T0, GPT-3, and Codex struggle in
zero-shot and few-shot learning for SKG. We also use UnifiedSKG to conduct a
series of controlled experiments on structured knowledge encoding variants
across SKG tasks. UnifiedSKG is easily extensible to more tasks, and it is
open-sourced at https://github.com/hkunlp/unifiedskg.
