  Grammatical error correction (GEC) is a task dedicated to rectifying texts
with minimal edits, which can be decoupled into two components: detection and
correction. However, previous works have predominantly focused on direct
correction, with no prior efforts to integrate both into a single model.
Moreover, the exploration of the detection-correction paradigm by large
language models (LLMs) remains underdeveloped. This paper introduces an
integrated detection-correction structure, named DeCoGLM, based on the General
Language Model (GLM). The detection phase employs a fault-tolerant detection
template, while the correction phase leverages autoregressive mask infilling
for localized error correction. Through the strategic organization of input
tokens and modification of attention masks, we facilitate multi-task learning
within a single model. Our model demonstrates competitive performance against
the state-of-the-art models on English and Chinese GEC datasets. Further
experiments present the effectiveness of the detection-correction structure in
LLMs, suggesting a promising direction for GEC.
