  Factual inconsistencies pose a significant hurdle for the faithful
summarization by generative models. While a major direction to enhance
inconsistency detection is to derive stronger Natural Language Inference (NLI)
models, we propose an orthogonal aspect that underscores the importance of
incorporating task-specific taxonomy into the inference. To this end, we
consolidate key error types of inconsistent facts in summaries, and incorporate
them to facilitate both the zero-shot and supervised paradigms of LLMs.
Extensive experiments on ten datasets of five distinct domains suggest that,
zero-shot LLM inference could benefit from the explicit solution space depicted
by the error type taxonomy, and achieves state-of-the-art performance overall,
surpassing specialized non-LLM baselines, as well as recent LLM baselines. We
further distill models that fuse the taxonomy into parameters through our
designed prompt completions and supervised training strategies, efficiently
substituting state-of-the-art zero-shot inference with much larger LLMs.
