CoEdIT: Text Editing by Task-Specific Instruction Tuning
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.
6465848702a171e703b31cb2
Vipul Raheja
claimed_verified
2023-05-19T19:03:46.032Z
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/avatars/3c37bf4b7c9db83a46af7c473ee4eb86.svg
Vipul Raheja
user
machineteacher
6465848702a171e703b31cb3
Dhruv Kumar
6465848702a171e703b31cb4
Ryan Koo
claimed_verified
2023-05-23T06:39:42.656Z
63ed75f3679c2cc40ab9e40f
/avatars/e70bc88e72082e5ad5a106bc92a27ab1.svg
Ryan Koo
user
rngusry
6465848702a171e703b31cb5
Dongyeop Kang
claimed_verified
2024-07-05T06:56:39.664Z
64356b40a4bd75c62cbc5926
/avatars/5f4c603464e9c8ad613a3a25fa4cacbf.svg
Dongyeop Kang
user
dykang
CoEdIT: Text Editing by Task-Specific Instruction Tuning
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.
CoEdIT, a fine-tuned large language model, enhances text editing quality and efficiency with diverse instructions, achieving state-of-the-art performance and user preference.
instruction-tuned LLMs instruction tuning text editing model writing assistance large language model fine-tuned diverse collection of task-specific instructions text editing benchmarks compositional comprehension abilities