Large Language Models (LLMs) have demonstrated remarkable performance in
various tasks and gained significant attention. LLMs are also used for local
sequence transduction tasks, including grammatical error correction (GEC) and
formality style transfer, where most tokens in a source text are kept
unchanged. However, it is inefficient to generate all target tokens because a
prediction error of a target token may cause a catastrophe in predicting
subsequent tokens and because the computational cost grows quadratically with
the target sequence length. This paper proposes to predict a set of edit
operations for the source text for local sequence transduction tasks.
Representing an edit operation with a span of the source text and changed
tokens, we can reduce the length of the target sequence and thus the
computational cost for inference. We apply instruction tuning for LLMs on the
supervision data of edit operations. Experiments show that the proposed method
achieves comparable performance to the baseline in four tasks, paraphrasing,
formality style transfer, GEC, and text simplification, despite reducing the
length of the target text by as small as 21\%. Furthermore, we report that the
instruction tuning with the proposed method achieved the state-of-the-art
performance in the four tasks.Comment: Work in progres