CoEdIT: Text Editing by Task-Specific Instruction Tuning
Authors: name: Vipul Raheja, name: Dhruv Kumar, name: Ryan Koo, name: Dongyeop Kang

Text editing or revision is an essential function of the human writing
process. Understanding the capabilities of LLMs for making high-quality
revisions and collaborating with human writers is a critical step toward
building effective writing assistants. With the prior success of LLMs and
instruction tuning, we leverage instruction-tuned LLMs for text revision to
improve the quality of user-generated text and improve the efficiency of the
process. We introduce CoEdIT, a state-of-the-art text editing model for writing
assistance. CoEdIT takes instructions from the user specifying the attributes
of the desired text, such as "Make the sentence simpler" or "Write it in a more
neutral style," and outputs the edited text. We present a large language model
fine-tuned on a diverse collection of task-specific instructions for text
editing (a total of 82K instructions). Our model (1) achieves state-of-the-art
performance on various text editing benchmarks, (2) is competitive with
publicly available largest-sized LLMs trained on instructions while being
sim60x smaller, (3) is capable of generalizing to unseen edit instructions,
and (4) exhibits compositional comprehension abilities to generalize to
instructions containing different combinations of edit actions. Through
extensive qualitative and quantitative analysis, we show that writers prefer
the edits suggested by CoEdIT, relative to other state-of-the-art text editing
models. Our code and dataset are publicly available.