title: CoEdIT: Text Editing by Task-Specific Instruction Tuning
summary: 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.
paper_authors._id: 6465848702a171e703b31cb2
paper_authors.name: Vipul Raheja
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-19T19:03:46.032Z
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paper_authors.avatarUrl: /avatars/3c37bf4b7c9db83a46af7c473ee4eb86.svg
paper_authors.fullname: Vipul Raheja
paper_authors.type: user
paper_authors.user: machineteacher
paper_authors._id: 6465848702a171e703b31cb3
paper_authors.name: Dhruv Kumar
paper_authors._id: 6465848702a171e703b31cb4
paper_authors.name: Ryan Koo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-23T06:39:42.656Z
paper_authors._id: 63ed75f3679c2cc40ab9e40f
paper_authors.avatarUrl: /avatars/e70bc88e72082e5ad5a106bc92a27ab1.svg
paper_authors.fullname: Ryan Koo
paper_authors.type: user
paper_authors.user: rngusry
paper_authors._id: 6465848702a171e703b31cb5
paper_authors.name: Dongyeop Kang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-05T06:56:39.664Z
paper_authors._id: 64356b40a4bd75c62cbc5926
paper_authors.avatarUrl: /avatars/5f4c603464e9c8ad613a3a25fa4cacbf.svg
paper_authors.fullname: Dongyeop Kang
paper_authors.type: user
paper_authors.user: dykang
paper_title: CoEdIT: Text Editing by Task-Specific Instruction Tuning
paper_summary: 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.
paper_ai_keywords: instruction-tuned LLMs CoEdIT state-of-the-art text editing model fine-tuned large language model task-specific instructions state-of-the-art performance text editing benchmarks compositional comprehension abilities qualitative and quantitative analysis