Text revision is necessary to harness the written-text following human-acceptable requirements. Multi-intent text revision, however, requires all potential textual defects to be addressed in the same computational model, which poses a new challenge to the traditional single-intent-based text revision modeling approach. Conventional approaches often rely on models tailored to specific edit intents, limiting their ability to address diverse or unseen edit intents. Inspired by the reasoning strengths of Large Language Models (LLMs), we introduce EditCoT, a novel framework for multi-intent text revision. EditCoT breaks down the revision process into sequential reasoning steps, each targeting a specific text defect. The structured approach can enhance LLMs’ editing capabilities by enabling precise, intent-specific revisions within a unified model. We evaluate the effect of EditCoT on multi-/single-intent text revision tasks. For multi-intent tasks, EditCoT achieves state-of-the-art results, with a SARI score of 65.80 and a BERTScore of 88.27. For single-intent tasks, EditCoT, paired with GPT-o1, presents a competitive performance compared to specifically fine-tuned models. Furthermore, when combined with GPT-o1 or DeepSeek, EditCoT demonstrates impressive transferability to new edit intents via custom edit-chains. Overall, this study offers an effective framework for modeling and resolving text editing tasks, contributing a multi-intent dataset and an augmented single-intent dataset to support the community in advancing text revision research.
Li et al. (Sat,) studied this question.