Large Language Models~(LLMs) have demonstrated incredible capabilities in
understanding, generating, and manipulating languages. Through human-model
interactions, LLMs can automatically understand human-issued instructions and
output the expected contents, which can significantly increase working
efficiency. In various types of real-world demands, editing-oriented tasks
account for a considerable proportion, which involves an interactive process
that entails the continuous refinement of existing texts to meet specific
criteria. Due to the need for multi-round human-model interaction and the
generation of complicated editing tasks, there is an emergent need for
efficient general editing models. In this paper, we propose
\underline{\textbf{G}}eneral \underline{\textbf{SP}}arse
\underline{\textbf{E}}fficient \underline{\textbf{E}}diting
Mo\underline{\textbf{D}}el~(\textbf{G-SPEED}), which can fulfill diverse
editing requirements through a single model while maintaining low computational
costs. Specifically, we first propose a novel unsupervised text editing data
clustering algorithm to deal with the data scarcity problem. Subsequently, we
introduce a sparse editing model architecture to mitigate the inherently
limited learning capabilities of small language models. The experimental
outcomes indicate that G-SPEED, with its 508M parameters, can surpass LLMs
equipped with 175B parameters. Our code and model checkpoints are available at
https://github.com/Banner-Z/G-SPEED.

通过单一模型实现多样化编辑需求并保持低计算成本，我们提出了 G-SPEED（General Sparse Efficient Editing Model），其中包括一种新颖的无监督文本编辑数据聚类算法和一种稀疏编辑模型架构，实验证明 G-SPEED 可以超越拥有 175B 参数的 LLMs。