  Few-shot event detection (ED) has been widely studied, while this brings
noticeable discrepancies, e.g., various motivations, tasks, and experimental
settings, that hinder the understanding of models for future progress.This
paper presents a thorough empirical study, a unified view of ED models, and a
better unified baseline. For fair evaluation, we compare 12 representative
methods on three datasets, which are roughly grouped into prompt-based and
prototype-based models for detailed analysis. Experiments consistently
demonstrate that prompt-based methods, including ChatGPT, still significantly
trail prototype-based methods in terms of overall performance. To investigate
their superior performance, we break down their design elements along several
dimensions and build a unified framework on prototype-based methods. Under such
unified view, each prototype-method can be viewed a combination of different
modules from these design elements. We further combine all advantageous modules
and propose a simple yet effective baseline, which outperforms existing methods
by a large margin (e.g., 2.7% F1 gains under low-resource setting).
