#  CoEdIT: Text Editing by Task-Specific Instruction Tuning

Vipul Raheja$\clubsuit$ Dhruv Kumar$\clubsuit$ Ryan Koo$\diamondsuit$ Dongyeop
Kang$\diamondsuit$
$\clubsuit$Grammarly $\diamondsuit$University of Minnesota
<EMAIL_ADDRESS>
###### Abstract

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 $\sim$60x 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 data are publicly
available111https://github.com/vipulraheja/coedit.

##  1 Introduction

Large language models (LLMs) have made remarkable progress toward generating
coherent text in a wide variety of tasks and domains to support writing
assistance Du et al. (2022a); Mallinson et al. (2022); Schick et al. (2023),
such as grammatical error correction Wu et al. (2023), text simplification
Štajner et al. (2022), paraphrasing Chowdhury et al. (2022), and style
transfer Reif et al. (2022). One of the emergent abilities of LLMs is the
capability to generalize to unseen tasks by following new or composed
instructions. Instruction-tuning, where LLMs are fine-tuned on a collection of
tasks phrased as instructions, makes the models more adept at interpreting and
following instructions, reducing the need for few-shot exemplars Sanh et al.
(2022); Ouyang et al. (2022b); Wei et al. (2022); Chung et al. (2022b)

Figure 1: Model comparison according to training parameters vs. average
performance across all text editing benchmarks reported in Tables 2 and 13.
Models available open-source are denoted with (*).

We aim to improve the capabilities of instruction-tuned models for text
editing by leveraging instruction-tuning from diverse tasks of text editing
benchmarks. Text editing is a complex and challenging task because human
writers cannot simultaneously grasp multiple demands and constraints of the
task while producing well-written texts Flower (1980); Collins and Gentner
(1980); Vaughan and McDonald (1986). Therefore, human writers tend to iterate
and revise their work multiple times to reduce the number of considerations at
each turn. As this process can be restrictive and open-ended, it poses a
challenge for text editing systems that power intelligent writing assistants.

While multiple previous works have attempted to develop general-purpose text
editing models using LLMs, they are either not trained with instruction-tuning
Du et al. (2022c); Kim et al. (2022), trained on much smaller models or not
trained on task-specific datasets Mallinson et al. (2022); Schick et al.
(2023), or are not publicly available Schick et al. (2023), which limits their
effectiveness, performance, or usability.

Figure 2: General-purpose (left) vs Task-specific (right) Instruction Tuning.

In this work, we introduce CoEdIT, a text editing system for writing
assistance with a natural language interface. A user can leverage CoEdIT by
providing natural language instructions such as "Paraphrase the sentence" or
"Fix the grammar", and edit their drafts based on the suggestions. These task-
specific instructions for text editing are more focused and dense than
general-purpose instructions, enabling them to generalize to composite and
new, adjacent text editing tasks, as shown in Figure 2. To build CoEdIT, we
fine-tune a pre-trained sequence-to-sequence model on a parallel corpus of
instruction-based 82K input-output pairs. The inputs and outputs are sourced
from publicly available corpora for different text editing tasks, and the
instructions are constructed based on rules that introduce lexical and
semantic variations.

Our main contributions are as follows:

  * •

We achieve state-of-the-art performance on multiple text editing tasks:
grammatical error correction, text simplification, iterative text editing, and
three stylistic editing tasks (formality style transfer, neutralization, and
paraphrasing).

  * •

We find that even our smallest but densified instruction-tuning model
outperforms other supervised text editing models, instruction-tuned models,
and general-purpose LLMs with nearly 60x greater parameters, on both manual
and automatic evaluations.

  * •

CoEdIT generalizes well to new, adjacent tasks not seen while fine-tuning, as
well as composite instructions with multiple task specifications.

  * •

Our data and models are publicly available in support of open-source research.

##  2 Related Work

Edit Intention |  Datasets |  Size |  Example Input |  Example Output
---|---|---|---|---
Fluency |  NUCLE-14 Lang-8 BEA-19 |  $20k$ |  Fix the grammar: When I grow up, I start to understand what he said is quite right. |  When I grew up, I started to understand what he said was quite right.
Coherence |  DiscoFuse |  $11k$ |  Make this text coherent: Their flight is weak. They run quickly through the tree canopy. |  Their flight is weak, but they run quickly through the tree canopy.
Clarity (Simplification) |  NEWSELA WikiLarge WikiAuto ParabankV2 IteraTeR-clarity |  $13k$ |  Rewrite to make this easier to understand: A storm surge is what forecasters consider a hurricane’s most treacherous aspect. |  A storm surge is considered a hurricane’s most dangerous aspect.
Style (Paraphrase) |  ParabankV2 |  $15k$ |  Paraphrase this: Do you know where I was born? |  Do you know my birthplace?
Style (Formalize) |  GYAFC |  $12k$ |  Write this more formally: omg i love that song im listening to it right now |  I love that song and I am listening to it at this moment.
Style (Neutralize) |  WNC |  $11k$ |  Write in a more neutral way: The authors’ exposé on nutrition studies. |  The authors’ statements on nutrition studies.
Table 1: Example data instances in the CoEdIT dataset (82K <instruction:
source, target> pairs). Instructional prompts in the inputs are italicized.

#### Large Language Models for Text Editing

In general, our work is related to many prior works that leverage LLMs; for
instance, finetuning T5 Raffel et al. (2020a) on pairs of original and edited
text Faltings et al. (2021); Reid and Neubig (2022); Mallinson et al. (2022);
Du et al. (2022a, b); Kim et al. (2022). However, these aforementioned works
are either not based on instruction tuning, use different modeling techniques
such as tag-based sequence labeling, or are not general enough to work on
multiple text editing tasks. Moreover, several LLMs are trained to solve
specific tasks only, such as grammar errors Mallinson et al. (2022); Fang et
al. (2023), text simplification Štajner et al. (2022), paraphrase generation
Chowdhury et al. (2022), or style transfer Reif et al. (2022), which limits
their generalizability.

#### Instruction Tuning for Writing Assistance

Explicitly teaching models how to follow natural language instructions is
closely related to recent work that has proposed benchmarks and frameworks for
fine-tuning models using large datasets of human-written instructions Wei et
al. (2022); Mishra et al. (2022); Sanh et al. (2022); Ouyang et al. (2022a);
Wang et al. (2022); Iyer et al. (2022); Bach et al. (2022); Longpre et al.
(2023). Recently, advanced data augmentation and instruction tuning, starting
with the Flan models Chung et al. (2022b), have shown that strong results stem
both from the larger and more diverse set of tasks. Additionally, enriching
task diversity and balancing task sources Sanh et al. (2022) are shown to be
critical to performance, suggesting instruction-tuned models offer a more
computationally-efficient starting checkpoint for downstream applications,
corroborating Liu et al. (2022) and Aribandi et al. (2022).

Regarding the research theme of instruction tuning for writing assistance, our
work is most closely related to PEER Schick et al. (2023), who fine-tuned
T5-based LLMs by following user-provided text-editing plans to perform the
said edits. There are a few significant differences in our approach compared
to PEER. While PEER attempts to either create or leverage a user-provided
plan, realize the edits conditioned on the plan, and try to explain the plan,
we focus only on the plan and edit parts of the pipeline. Even when it comes
to handling the set of editing plans in the form of natural language
instructions, our work focuses on edits that do not add new information.
Therefore, we compare our models only against PEER-Edit models.

Our work is closest to DElIteraTeR Kim et al. (2022) in terms of leveraging
datasets from various text editing tasks, which, in their work, are then used
for training an iterative text editing model using a tag-and-edit approach. As
we focus on non-meaning-changing edits similar to Kim et al. (2022), we
broaden the scope to accommodate for some meaning change since some of the
style-related tasks such as neutralization, require such edits by definition.
Specifically, we extend the style category proposed by Du et al. (2022b) and
Kim et al. (2022) to include tasks such as paraphrasing, formality style
transfer, and neutralization. Our experiments have shown that instruction
tuning with multiple tasks is more effective than multi-task fine-tuning,
making the instruction space denser and reinforcing the complementary effects
of multiple tasks.

Finally, no prior works, to the best of our knowledge, have investigated the
compositionality aspect of instruction-tuned LLMs for text editing.

##  3 CoEdIT: Text Editing by Instruction Tuning

###  3.1 Training Dataset

Our dataset creation is based on the IteraTeR+ dataset proposed by Kim et al.
(2022) who combined datasets from various text editing tasks (See Table 1).
Their work, in turn, is based on Du et al. (2022b), who categorized each edit
into meaning-changed or non-meaning-changed. Edits that belong to the latter
group are further assigned to fluency, coherence, clarity, or style. The
aforementioned taxonomy of edit intents from IteraTeR reflects writers’
general intention behind their revision, providing more in-depth information
than just superficial edit operations, such as add and delete.

Similar to Kim et al. (2022), our work focuses on non-meaning-changed edits.
However, we consider those edits to be ones that do not add new information or
perform fact updates. Since the style edit intention is quite subjective in
nature, we allow for the possibility of meaning change so as to fulfill the
needs of making stylistic edits, but we constrain the editing tasks to ensure
the edited texts are semantically similar to the sources, and not to the
extent of adding new information or fact updates. With this in mind, we expand
the style edit intention category from IteraTeR+ to include four new sub-
intentions: Paraphrasing, Formality Style Transfer (or Formalization), and
Neutralization.

The aforementioned IteraTeR dataset taxonomy lends itself conveniently to be
articulated as natural language instructions and allows us to naturally
formulate them into instructional prompts (See Table 1). We rewrite each edit
intention as a set of natural language instruction prompts to create the
CoEdIT dataset. To allow models to adapt to linguistic variations of the
instructions, we also include paraphrases of the instruction templates, e.g.,
instead of “Write" we also use “Generate” or "Rewrite", or instead of
“Paraphrase the text” we use “Rewrite the text with different wording", and so
on. For each task, we develop a variety of such diverse instructional prompts,
and randomly sample an instruction from the aforementioned group of task-
specific instruction candidates to be pre-pended to the source in order to
form an <instruction: source, target> data pair. We provide the full list of
our instructional prompts in Appendix B. In total, our training dataset
consists of around 82K <instruction: source, target> pairs. We keep the
original train-validation-test splits consistent as the original datasets, but
diversify the train and validation splits with the paraphrasing augmentations.
The details of datasets and instructions used to train our models is described
in Appendix A.

Figure 3: Illustration of the CoEdIT training pipeline.

###  3.2 Text Editing Models

We fine-tune different versions of pre-trained FlanT5 Chung et al. (2022a)
models on the CoEdIT dataset using cross-entropy loss. Specifically, we use
FlanT5-L (770M parameters), FlanT5-xl (3B parameters), FlanT5-xxl (11B
parameters) models, which are henceforth referred to as CoEdIT-l, CoEdIT-xl,
and CoEdIT-xxl respectively. The training details are summarized in Appendix C

##  4 Experimental Setup

We conduct experiments to determine if a standard instruction-tuned language
model fine-tuned using task-specific data can improve text editing
performance, and to determine if it can further generalize into a general-
purpose text editing model, capable of following human-written instructions
and handling a wider array of editing tasks, such as unseen and compositional
instructions.

Specifically, we aim to answer the following research questions:

  * •

RQ1: Can CoEdIT follow text editing instructions and perform high-quality
edits across a wide variety of tasks?

  * •

RQ2: Is CoEdIT generalizable to perform high-quality edits for new types of
text editing instructions?

  * •

RQ3: Does CoEdIT make the writing process more efficient and effective for
human writers?

We answer these questions via quantitative analyses of model outputs (Section
5) and via qualitative analyses and human evaluations of model outputs
(Section 6). Further, we investigate RQ2 along two dimensions: (1)
compositional generalization to instructions containing combinations of
multiple different kinds of edits, and (2) out-of-domain generalization to
instructions with new task requirements on previously unseen data.

###  4.1 Models

#### Baseline

We first evaluate our models with a no-edits baseline. Its output is simply a
copy of the source input without the instruction. This strategy performs
reasonably on tasks where the target output largely overlaps with the input
(e.g., GEC).

#### Supervised Text Editing Models

We also evaluate existing LLMs for text editing that are not fine-tuned with
instruction-specific data. Specifically, to understand the effect of task-
specific fine-tuning, we train and evaluate against T5222As a consequence of
its pre-training objective of infilling, the original T5 model cannot continue
text well. Therefore, similar to Schick et al. (2023), we evaluate its LM
Adapted versions Lester et al. (2021), which are further trained with a
language modeling objective. Raffel et al. (2020b) models as primary
alternatives of our Flan-T5 models. Additionally, we also compare our models
against IteraTeR Du et al. (2022b) and DElIteraTeR Kim et al. (2022), which
have shown strong performance on a variety of text editing tasks.333We are
unable to make full comparisons against EdiT5 Mallinson et al. (2022) and PEER
Schick et al. (2023) as the models are not publicly available.

#### Instruction-tuned LLMs

A major group of our comparisons is against instruction-tuned LLMs:

  * •

PEER Schick et al. (2023) is our main point of comparison. More specifically,
due to the focus of our work on improving revision quality (Section 2), we
compare against PEER-Edit as our models are largely trained to not change the
meaning or the informational content of the input text.

  * •

T0, T0++ Sanh et al. (2022) and T$k$-Instruct Wang et al. (2022), which are
all initialized from the LM Adapted variant of T5. The T0 models are fine-
tuned using a variety of human-written prompts from PromptSource Bach et al.
(2022), whereas T$k$-Instruct is fine-tuned on Super-NaturalInstructions Wang
et al. (2022), a collection of instructions for more than 1,600 tasks,
including grammatical error correction and text simplification. Unlike Super-
NaturalInstructions, PromptSource does not directly contain editing tasks, but
related tasks including summarization and data-to-text generation.

  * •

Alpaca Taori et al. (2023) is an instruction-tuned version of the 7B parameter
LLaMA model Touvron et al. (2023) trained on 52K instruction-following
demonstrations generated by GPT3.

  * •

We also compare InstructGPT Ouyang et al. (2022a), a variant of GPT3 that was
fine-tuned via reinforcement learning on a large dataset of instructions and
corresponding outputs written by humans.444We use text-davinci-003

  * •

GPT3.5 (henceforth referred to as ChatGPT), is an improved version of
InstructGPT optimized for chat. We utilize OpenAI’s API for all inference
tasks.555We use gpt-3.5-turbo

  * •

GPT3 also offers a text Editing API666We use text-davinci-edit-001 (we refer
to as GPT3-Edit), which works by specifying existing text as a prompt and an
instruction on how to modify it. According to the API
docs777https://platform.openai.com/docs/api-reference/edits, the edits
endpoint can be used to change the tone or structure of text, or make targeted
changes like fixing spelling, which is directly comparable to the tasks we
train CoEdIT to perform.

#### Large-Pretrained Decoder-only Models

We compare LLMs with no instruction tuning:

  * •

The original 175B GPT3888We use davinci Brown et al. (2020) model that is not
instruction-tuned demonstrates strong general-purpose text revision
capabilities.

  * •

BLOOM Workshop (2023) is an open-source large auto-regressive language model
released by BigScience trained on the ROOTs corpus, which is comprised of 46
different languages and 13 programming languages. Due to computing
constraints, we utilize their 7B model as opposed to their largest 176B model.

  * •

LLaMA Touvron et al. (2023) is Meta AI’s general-purpose language model
trained only on publicly available data. In parallel to its instruction-tuned
counterpart (Alpaca) and due to computing constraints, we only compare against
the 7B model as opposed to their largest model (65B).

We perform experiments with these models in two settings: zero-shot and few-
shot (by providing specific editing examples during prompting). For few-shot
evaluations, the inputs were constructed by randomly sampling 4 examples from
the CoEdIT finetuning dataset by showing the input sentence and its
corresponding revised reference pre-pended to the instructional prompt. We
conduct few-shot experiments for two decoder-only LLMs (GPT3 and BLOOM) and
three instruction-tuned LLMs (InstructGPT, ChatGPT, and Alpaca). Outputs of
all models were generated using greedy decoding for all models unless
specified otherwise.

###  4.2 Test Datasets

To assess the editing capabilities of CoEdIT, we perform evaluations on
standard test sets sourced from a wide variety of text editing task
benchmarks, most notably, EditEval Dwivedi-Yu et al. (2022). Owing to the
overlap of our work with PEER, we keep our evaluation datasets and evaluation
metrics as close to theirs as possible for consistency. Specifically, we
consider the following datasets:

#### Grammatical Error Correction

We use the JFLEG Napoles et al. (2017) corpus of English sentences that
represents a range of language proficiency levels and utilizes comprehensive
fluency edits to enhance the accuracy of the evaluation. For evaluation, we
use the GLEU Napoles et al. (2015) score as the primary metric and also report
results using the SARI Xu et al. (2016) metric that averages match scores for
the three word-level edit operations add, delete and keep.

#### Text Simplification

We use the TurkCorpus Xu et al. (2016) and ASSET Alva-Manchego et al. (2020)
datasets, which were both created from WikiLarge data Zhang and Lapata (2017)
using the same 2000 validation and 359 test source sentences, where each
complex sentence consists of multiple crowd-sourced reference simplifications.
Specifically, TurkCorpus contains eight reference simplifications, and ASSET
contains ten references per source sentence. We report results using the SARI
metric for Simplification tasks.

#### Coherence

We evaluate coherence on two datasets: the Coherence split of IteraTeR Du et
al. (2022b), and the DiscoFuse dataset Geva et al. (2019), as it involves
linking two given sentences as coherently as possible using edit operations
such as inserting discourse connectives. These datasets are sourced from
different domains such as ArXiv, News, Wikipedia, and Sports articles. We
report SARI as well for Coherence tasks.

#### Iterative Text Editing

We use IteraTeR Du et al. (2022b), which is an iterative text revision dataset
spanning five edit intentions (Section 3) across three different domains
(ArXiv, News, Wikipedia) for iteratively revised text. We evaluate our models
on the IteraTeR dataset using the SARI metric. We report the performance on
individual intentions – Fluency, Clarity, and Coherence, and also aggregated
scores on the full dataset, which includes Style edits.

The rest of the section describes the evaluation setups for Style-related
edits:

#### Formality Style Transfer

We use Grammarly’s Yahoo Answers Formality Corpus (GYAFC) Rao and Tetreault
(2018) which is a parallel corpus of informal and formal sentence pairs from
two different domains, for the task of formality style transfer (rewriting
informal texts with a more formal style). Similar to prior works, we evaluate
the quality of rewriting using SARI, and the accuracy of style transfer using
a formality classification
model999https://huggingface.co/s-nlp/xlmr_formality_classifier.

#### Neutralization

We use WNC Pryzant et al. (2020), a dataset from the Subjective Bias
Neutralization task, where the objective is to remove or mitigate biased words
to make sentences more neutral. Based on prior works, we use ExactMatch (EM)
for evaluations, which is the percentage of examples for which the performed
edit exactly matches a given target.

#### Paraphrasing

We use the widely-used Microsoft Research Paraphrase Corpus (MRPC) Dolan and
Brockett (2005), the STS benchmark from SemEval-2017 (STS) Cer et al. (2017),
and the Quora Question Pairs101010https://quoradata.quora.com/First-Quora-
Dataset-Release-Question-Pairs (QQP) datasets for the task of paraphrasing,
which is defined as making edits to a given text such that it is not identical
to the original, but shares the same semantic meaning. As such, we evaluate
paraphrasing on two metrics: Self-BLEU Zhu et al. (2018) and Semantic
Similarity111111We use the paraphrase-mpnet-base-v2 model from
SentenceTransformers Reimers and Gurevych (2019). We report Self-BLEU to
measure the diversity of the paraphrases relative to the given source and
reference texts and the semantic similarity to evaluate the meaning
preservation.

|  Model | Size |  Overall |  IteraTeR |  Fluency |  Clarity |  Coherence | Style
---|---|---|---|---|---|---|---|---
|  |  IteraTeR↑ |  JFLEG↑ |  ASSET↑ |  DiscoFuse-Wiki↑ |  GYAFC(↑/↑) |  WNC(↑/↑) |  MRPC(↓/↑)
(a) |  Copy | - |  27.6 |  29.8 |  26.7 / 40.5 |  20.7 |  30.8 |  17.6 / 10.6 |  31.85 / 0 | 47.4 / 100
|  T5-large | 770M |  24.7 |  21.1 |  32.7 / 22.9 |  35.8 |  28.01 |  30.9 / 4.89 |  13.2 / 0 | 27.6 / 62.8
|  T0* | 3B |  29.7 |  26.1 |  42.2 / 36.1 |  33.2 |  32.4 |  37.9 / 39.3 |  19.4 / 0 | 28.3 / 84.1
(b) |  Tk-Instruct* | 3B |  27.3 |  21.0 |  35.2 / 26.8 |  36.9 |  28.9 |  35.7 / 43.01 |  24.2 / 0.1 | 20.4 / 48.9
|  T0++* | 11B |  32.6 |  31.5 |  39.4 / 40.5 |  33.1 |  35.5 |  36.8 / 43.7 |  21.2 / 0 | 42.9 / 94.9
|  BLOOM | 7B |  28.3 |  31.3 |  40.0 / 41.0 |  22.0 |  32.3 |  18.7 / 74.2 |  21 / 0.1 | 6.4 / 75.2
(c) |  LLaMA | 7B |  28.2 |  30.1 |  27.7 / 3.34 |  21.8 |  31.1 |  18.8 / 89.1 |  31.9 / 0 | 5.29 / 64.2
|  GPT3 | 175B |  27.4 |  23.3 |  38.1 / 2.8 |  34.8 |  26.2 |  36.6 / 87.9 |  23.4 / 0 | 0 / 51.7
|  Alpaca | 7B |  28.4 |  30.4 |  28.5 / 6.4 |  22.0 |  31.1 |  18.9 / 94.4 |  31.9 / 0 | 0 / 77.9
(d) |  ChatGPT | - |  36.9 |  28.2 |  57.6 / 49.4 |  45.9 |  40.2 |  40.7 / 99.6 |  28.5 / 0.1 | 13.4 / 99.0
|  InstructGPT | 175B |  41.6 |  32.6 |  62.4 / 57.2 |  44.6 |  47.4 |  47.8 / 98.2 |  33.7 / 0.1 | 16.03 / 98.9
|  GPT3-Edit | 176B |  41.8 |  36.1 |  52.4 / 50.6 |  32.9 |  54.0 |  35.7 / 52.3 |  50.7 / 17.1 | 22.6 / 98.7
|  Alpaca | 7B |  30.0 |  30.8 |  33.03 / 11.3 |  23.2 |  33.1 |  20.6 / 95.4 |  32.04 / 0 | 0.1 / 66.7
|  BLOOM | 7B |  28.2 |  30.1 |  28.5 / 3.6 |  21.0 |  31.2 |  18.6/ 63.9 |  31.9 / 0 | 0.16 / 84.8
(e) |  GPT3 | 175B |  38.4 |  32.4 |  50.1 / 4.1 |  39.2 |  45.1 |  43.1 / 97.2 |  36.7 / 0 | 0 / 14.5
|  ChatGPT | - |  40.1 |  30.8 |  58 / 50.6 |  45.4 |  51.2 |  42.3 / 99.6 |  34.1 / 0 | 13.3 / 96.1
|  InstructGPT | 175B |  45.1 |  36.2 |  64.5 / 55.7 |  46.3 |  55.2 |  47.3 / 98.8 |  42.8 / 0 | 15.9 / 99.5
|  IteraTeR | 570M |  31.0 |  32.8 |  35.9 / 34.3 |  21.8 |  30.1 |  22.7 / 54.1 |  34.2 / 0 | 40.5 / 97.8
(f) |  DelIteraTeR | 570M |  28.0 |  29.9 |  27.5 / 31.2 |  21.2 |  32.2 |  18.1 / 57.8 |  31.9 / 0 | 39.1 / 100
|  PEER-3B* | 3B |  41.7 |  37.1 |  55.5 / 54.3 |  30.5 |  - |  - |  53.3 / 21.6 | -
|  PEER-11B* | 11B |  42.1 |  37.8 |  55.8 / 54.3 |  29.5 |  - |  - |  54.5 / 22.8 | -
|  CoEdIT-l | 770M |  49.8 |  35.2 |  62.4 / 59.3 |  42.4 |  75.3 |  54.6 / 98.0 |  69.3 / 46.4 | 23.3 / 99.1
(g) |  CoEdIT-xl | 3B |  51.4 |  36.6 |  64.5 / 60.7 |  42.2 |  80.5 |  55.1 / 98.3 |  70.4 / 48.8 | 21.3 / 99.6
|  CoEdIT-xxl | 11B |  51.5 |  37.1 |  65.0 / 61.5 |  41.7 |  78.6 |  55.1 / 97.2 |  71.0 / 51.4 | 21.8 / 99.0
Table 2: Comparison of CoEdIT against various baselines, divided into seven
groups: (a) a copy baseline and T5-large baseline prefixed only with task-
specific tags (i.e. <gec>, <clarity>, etc.) (b) T5-based models, (c) Decoder-
only LLMs, (d) Instruction-tuned LLMs, (e) Prompt-tuned versions of large pre-
trained models (f) SOTA text editing models, and, (g) Variants of CoEdIT
models (our work). The first score for each task (excluding MRPC style task)
are SARI scores. The second scores for Fluency, GYAFC, and WNC are GLEU,
Formality Transfer accuracy (%), and EM, respectively. For MRPC, the first
score is Self-BLEU, while the second score is semantic similarity.
($\uparrow$) indicates higher is better, and ($\downarrow$) indicates lower is
better. The best performing models131313Since PEER had several scores missing,
and due to the high scores of paraphrasing transfer, for fairness, it was left
out of the Overall score calculations. For results with multiple metrics, the
best-performing method is calculated by taking the average. For the MRPC
average, we subtract the Self-BLEU score from 100 since lower is better. for
each dataset are highlighted in bolded boxes. Results marked with (*) are ones
reported in prior works. Results on the remaining datasets are present in
Table 13.

##  5 Quantitative Results

###  5.1 Text Editing Performance

Table 2 helps us answer RQ1 by comparing the performance of CoEdIT to other
models across various text editing tasks. For clarity, we first present
results from the more well-known evaluation sets here and present additional
results (i.e. sub-tasks and additional datasets) in Table 13.

We segregate models into seven groups. The first group (a) consists of the
copy baseline and T5-Large baseline prefixed with only task-specific tags,
while the second group (b) consists of instruction-finetuned T5-based models
on non-text-editing tasks. We find that CoEdIT substantially outperforms these
models across all tasks.

The next two groups (c, d) show different LLMs varying from 7B to 176B
parameters in size. Those present in the third group (c) are decoder-only
models, while those in the fourth group (d) are instruction-tuned. We find
that CoEdIT outperforms all LLMs comparable to its model size (such as BLOOM,
Alpaca, and Llama) across all tasks. Compared to larger models such as ChatGPT
and InstructGPT, our models outperform them on most tasks except
simplification (ASSET) and paraphrasing (MRPC). It shows how current general-
purpose LLMs and instruction-tuned models are underfitted, so it would be more
important to construct a more dense task/instruction space rather than scaling
up the model size, resulting in a better balance between model size and task
performance.

Although models such as Alpaca and T5-based models (Tk-instruct, T0, T0++)
have previously shown strong capabilities for zero-shot tasks, we hypothesize
it may be due to the difficulty of instructional prompts that results in
weaker performance compared to CoEdIT. In particular, we see that the decoder-
only models often repeat the input for more complex tasks, such as ones under
the Style intent group, which is similarly demonstrated by BLOOM, LLaMA and
GPT3. This can be attributed to difficulty understanding the prompted task
that results in the model either repeating the input or simply generating a
continuation of the input sentence unrelated to the task.

|  Model | Size |  IteraTeR |  Fluency |  Clarity |  Coherence | Style
---|---|---|---|---|---|---|---
|  IteraTeR↑ |  JFLEG↑ |  ASSET↑ |  DiscoFuse-Sport↑ |  GYAFC(↑/↑) |  WNC(↑/↑) |  MRPC(↓/↑)
|  CoEdIT-xl | 3B |  36.6 |  64.5 / 60.7 |  42.2 |  80.5 |  55.1 / 98.3 |  70.4 / 48.8 | 21.3 / 99.6
(a) |  T5-xl (prefix) | 3B |  34.3 |  61.8 / 58.6 |  41.0 |  71.4 |  50.7 / 94.6 |  62.7 / 33 | 30.6 / 87.4
(b) |  FlanT5-xl | 3B |  30.2 |  28.3 / 41.3 |  25.5 |  37.9 |  25.0 / 27.8 |  33.5 / 0.0 | 46.6 / 92.5
(c) |  CoEdIT-xl-R | 3B |  33.9 |  63.8 / 60.2 |  36.2 |  69.7 |  52.6 / 48.7 |  69.2 / 25.4 | 35.4 / 92.6
Table 3: Ablation results for CoEdIT to evaluate the impact of (a) instruction
tuning (b) task-specific training, and (c) quality of instructions. The scores
from left to right follow exactly as Table 2.

Next, in the fifth group (e), we evaluate the LLMs under a few-shot setting.
We observe that giving specific examples helps improve performance in all
models except BLOOM. This might be because BLOOM is a completion model not
suited for revision tasks. Furthermore, we see a decrease in performance for
some tasks, particularly MRPC for GPT3. This may be because when GPT3 is
prompted with few-shot examples, it tends to repeat its generation
continuously, which results in a low BLEU score but low semantic similarity as
well. This is similarly the case for BLOOM and Alpaca. We don’t present any
experiments for GPT3-Edit under the few-shot setting, as scores tended to stay
the same across all tasks – implying that GPT3-Edit may not have as good in-
context learning capabilities. Overall, we find that even our smallest 770M
parameter model is competitive against LLMs evaluated in a few-shot setting in
most tasks and even outperforms them on Coherence, Formality and
Neutralization tasks.

In the final group (f), we compare our models against task-specific text
editing models such as ITeraTeR, DelITeraTeR, and PEER. ITeraTeR and
DelITeraTeR perform comparatively worse than the scores reported in the
original paper as we present different and more difficult inputs, only pre-
pending instructions to the inputs while ITeraTeR and DelITeraTeR were trained
with task-specific tags. Furthermore, ITeraTeR and DelITeraTeR were trained
using BART and Pegasus, both of which have a summarization pre-training
objective and were not trained to follow instructions, while CoEdIT utilizes a
T5-based model, which can better understand tags and is also instruction-
tuned. On average, CoEdIT beats PEER across all reported evaluations except
the IteraTeR benchmark. This can primarily be attributed to the difference in
task-specific fine-tuning since PEER used Wikipedia as the source of
instructional edit data.

###  5.2 Ablation Studies

Table 3 shows the performance of various baselines, which we discuss in detail
in this section.

#### Instruction Tuning.

To understand the effectiveness of instruction-tuning, we fine-tune the 3B
parameter T5 model (T5-xl) and compare it with CoEdIT-xl, its FlanT5
counterpart on the same training and validation sets. The only change is that
the instructional prompts for the training datasets are replaced by task-
specific prefixes. Specifically, the 82$k$ <instruction: source, target> pairs
in the training dataset used to train the CoEdIT models were modified to
<task: source, target>, where task was one of gec, simplify, clarify,
coherence, formalize, neutralize and paraphrase. We observe (Table 3(a)) that
the instruction-tuned CoEdIT models consistently outperform prefix-tuned T5
models, confirming the effectiveness of instruction-tuning over prefix-tuning.

#### Task-Specific Training.

A core contribution of this work is to push the performance of small- (<1B
parameters) to medium-sized (1-10B parameters) LLMs for common text editing
tasks. This drives the need for fine-tuning on task-specific datasets. The
impact of this task-specific data augmentation for text editing tasks has
already been shown in Kim et al. (2022). For this work, we compare our task-
specific fine-tuned models against their FlanT5 un-tuned counterparts referred
to as FlanT5-xl (Table 3(b)). We see a substantial gap between the two for all
datasets and model sizes, thus, confirming prior findings.

Instruction |  GPT3-Edit |  CoEdIT-xl (ours)
---|---|---
Fix all grammatical errors: Every one would expect to leave his owns and secrets dreams like to be a painter, a writter … |  Every one would expect to leave his owns and secrets dreams like the painting and the write … |  Every one would expect to leave his own and secret dreams like to be a painter, a writer…
Make this simpler: Fives is a British sport believed to derive from the same origins as many racquet sports. |  Fives is a British racquet sport. |  Fives is a British sport. It is thought to have come from the same place as many racquet sports.
Fix coherence in this text: Phantom Budo is an incredibly powerful ability. Spike has yet to fully develop this awesome fighting technique. |  Phantom Budo is an incredibly powerful fighting technique. Spike is yet to fully develop it . |  Phantom Budo is an incredibly powerful ability, but Spike has yet to fully develop it.
Use different wording: Judge Gerald W. Heaney, in dissent, said the authorities should have allowed the prisoner to be medicated without the consequence of execution. |  Judge Gerald W. Heaney, who disagreed with the majority decision, said the prisoner should be medicated after important testimony is received. |  In his dissent, Gerald W. Heaney said the authorities should have allowed the prisoner to be treated without execution.
Table 4:  A sample of text revisions generated by GPT3-Edit and CoEdIT-xl
(ours).

#### Quality of Instructions.

While we developed with a limited set of task-specific instructional prompts,
there has been widespread work on the prompt sensitivity of LLMs, especially
with growing model capacity Lu et al. (2022). To assess the robustness of
CoEdIT models on instructional prompts, we train another baseline CoEdIT-xl
model with randomized task-specific instructions (henceforth referred to as
CoEdIT-xl-r). Specifically, we randomized the full training dataset where for
a given data point, we replaced the instruction for that task with a randomly
chosen instruction from a different task. Table 3(c) shows the results for
this experiment. We observe that while CoEdIT-xl-r achieves scores that are
higher than the non-task-specific tuned FlanT5-xl (especially on edit-based
metrics such as SARI), it significantly falls behind CoEdIT-xl on those, as
well as on the style accuracy metrics such as formality transfer accuracy and
paraphrasing semantic similarity. This indicates that while the instructional
structure of the inputs and task-specific training makes the model learn how
to make edits (which drives up the SARI scores), however, the accuracy of
those edits suffers since they are trained with the wrong instructions most of
the time. Overall, the improvements highlight the positive impact of task-
specific training, and the gaps in performance highlight the negative impact
of lack of proper instruction tuning.

##  6 Qualitative Results

CoEdIT-xl | GPT3-Edit | Tie | Neither
---|---|---|---
64% | 10% | 4% | 22%
Table 5:  Human evaluation results: Pair-wise comparison of our 3B-parameter
model (CoEdIT-xl) against the best-performing 175B-parameter instruction-tuned
LLM for text editing (GPT3-Edit) for 50 inputs. The numbers indicate the
percentage of test inputs for which the human annotators preferred the said
model.

We address RQ2 and RQ3 (Section 4) in this section. We show that CoEdIT shows
generalization abilities to adjacent tasks not seen during fine-tuning, and
can be extended to generalize to composite instructions containing a
combination of tasks. Further, our human evaluation studies show that expert
human evaluators find the text generated by CoEdIT to be of higher quality
than a much larger instruction-tuned LLM.

###  6.1 Text Editing Quality

Since text editing is often subjective, and automatic metrics are not always
accurate in measuring if an instruction is satisfied, we conduct human
evaluations for our model outputs by proficient linguists to ensure they meet
the instructional constraints. Given the automatic evaluation results in
Section 5, we compare our 3B-parameter CoEdIT-xl model against the largest
comparable 175B instruction-tuned LLM for text editing GPT3-Edit.
Specifically, we conducted a pairwise comparison: each annotator was shown an
instructional input and outputs from both models (they were not aware which
output is generated by which model). They were then asked to evaluate the
fluency, accuracy, and meaning preservation of the edited texts and choose the
higher-quality output ("neither" and "tie" are also valid options). We collect
three annotations for each question and use the majority vote as the final
judgment.

Table 5 shows the results of the evaluation. The annotators prefer our CoEdIT
model for 64% of the inputs, whereas, for 10% of the inputs, GPT3-Edit’s
output is preferred. In 4% cases, both models produce equally good outputs,
whereas, for 22% of the inputs, both models generate unacceptable outputs.
Table 4 provides a side-by-side comparison of the outputs generated by the two
models.

###  6.2 Generalizability to out-of-domain tasks

We analyze the generalization capabilities of our models by evaluating them on
a few tasks that do not exist in the fine-tuning data. Specifically, we chose
two standard NLP tasks – sentence compression Filippova and Altun (2013) and
politeness transfer Madaan and Yang (2021) for this study. It is noteworthy
that while our models were not fine-tuned on these exact tasks, we chose them
so that the models could still comprehend them based on the other tasks they
were fine-tuned on. We define them as being "adjacent" tasks, which still
exist within the scope of existing tasks but have not been seen during fine-
tuning (blue dotted lines in Figure 2).

Our rationale for choosing these adjacent tasks is that for many real-world
text editing tasks, a high degree of generalizability to arbitrary
instructions (such as creative writing or non-standard style transfer, where
it is hard to evaluate the quality of edits) is not that critical, especially
for tasks such as GEC and Coherence, where the input and output texts have
relatively high degrees of overlap. Another hypothesis is that since these
tasks have a certain degree of overlap in the nature of both instructions and
edits (eg. politeness transfer is similar in nature to formality style
transfer and detoxification, and sentence compression is similar in nature to
text simplification, albeit with simpler and stricter requirements on output
lengths), it is possible for the models to generalize to these tasks.

Similar to the previous experiment, in addition to GPT3-Edit, we compare our
3B-parameter CoEdIT-xl model against similarly-sized prefix-tuned T5 model
(T5-xl), and the non-task-specific trained FlanT5-xl model (same models as the
ones used in Table 3 (a) and (b)). For evaluation, we curated a set of new
instructional prompts geared towards both the new tasks (details in Appendix
B). We evaluated the models on the respective test datasets from Filippova and
Altun (2013) and Madaan and Yang (2021).

|  Sentence | Politeness
---|---|---
|  Compression | Transfer
Model |  SARI↑/CR(%)↑ |  S-BLEU↓/TA(%)↑
GPT3-Edit |  23.98 / 6.09 | 63.31 / 63.11
T5-xl (prefix) |  31.47 / 7.66 | 81.43 / 58.82
FlanT5-xl |  33.21 / 15.29 | 91.91 / 52.69
CoEdIT-xl |  35.17 / 22.78 |  60.32 / 64.45
Table 6:  Comparison of our 3B-parameter model (CoEdIT-xl) against the best-
performing non-instruction-tuned model (T5-xl) and non-task-specific-tuned
model (FlanT5-xl) on out-of-domain generalization for two tasks: Sentence
compression and Politeness Transfer. We also compare against the
175B-parameter instruction-tuned GPT3 model for text editing (GPT3-Edit). CR
indicates Compression Ratio (%), and TA indicates Transfer Accuracy (%).

Table 6 shows the results of CoEdIT against various models on the sentence
compression and politeness transfer tasks. We report the SARI metric for
rewrite quality, and compression ratio for the task-specific quality. For the
politeness transfer task, we report Self-BLEU Zhu et al. (2018) for the
rewrite quality141414We report Self-BLEU based on the original paper, since
there are no references provided in the dataset. and Transfer Accuracy for the
task-specific quality. We observe that CoEdIT consistently outperforms other
models on both tasks, which indicates its generalization abilities on these
new and unseen adjacent tasks. It is noteworthy that GPT3-Edit performs quite
well out-of-the-box on the politeness transfer task, but not so much on the
compression task.

Model | Size |  IteraTeR |  Fluency |  Clarity |  Coherence | Style
---|---|---|---|---|---|---
IteraTeR↑ |  JFLEG↑ |  ASSET↑ |  DiscoFuse↑ |  GYAFC(↑/↑) |  WNC(↑/↑) |  MRPC(↓/↑)
CoEdIT-xl | 3B |  36.6 |  64.5 / 60.7 |  42.2 |  80.5 |  55.1 / 98.3 |  70.4 / 48.8 | 21.3 / 99.6
CoEdIT-xl-C | 3B |  36.5 |  65.1 / 61.3 |  42.0 |  74.8 |  55.9 / 97.2 |  69.7 / 48.5 | 20.7 / 98.8
Table 7: Results for composite prompt training on single-task performance. The scores from left to right follow exactly as Table 2.  CoEdIT-xl-C | GPT3-Edit | Tie | Neither
---|---|---|---
38% | 34% | 3% | 25%
CoEdIT-xl-C | CoEdIT-xl | Tie | Neither
34% | 21% | 14% | 31%
Table 8:  Human evaluation results: Pair-wise comparison of our 3B-parameter
model for composite instructions (CoEdIT-xl-C) against the best-performing
175B-parameter instruction-tuned LLM for text editing (GPT3-Edit) and against
the equivalent 3B-parameter CoEdIT-xl (with chaining pipeline) model for 30
composite instructions. The numbers indicate the percentage of test inputs for
which the human annotators preferred the said model.

###  6.3 Generalizability to composite tasks

Finally, we also explore the capability of our model to understand composite
natural language instructions. Composite instructions are made up of a
combination of tasks. For example, for the composite instruction, "Make the
text simpler, paraphrase it, and make it formal", the model needs to
simultaneously perform simplification, paraphrasing and formalization of the
input sentence.

Since there is no publicly available dataset for composite instructions, we
create the CoEdIT-Composite dataset by expanding the CoEdIT dataset. In
addition to the single-task instructions, we use seven new combinations of
instructions. Each composite instruction has either two or three tasks. It has
90K <instruction: source, target> pairs. More details for the dataset are
present in Appendix A. We then fine-tune the FlanT5-xl model on the CoEdIT-
Composite dataset, which we refer to as CoEdIT-xl-C. The training details are
similar to CoEdIT-xl and are summarized in Appendix C.

We evaluate CoEdIT-xl-C on both single and composite instructions. For the
single instructions, we use automated metrics and the same test setup used to
evaluate the other models in Table 2. Table 7 shows the results comparing
CoEdIT-xl-C to CoEdIT-xl. We find that the overall performance is on par with
that of CoEdIT-xl. This shows that training the model on composite prompts not
only gives the model the capability to carry out multiple tasks but does so
without having an impact on single-task performance.

For evaluating the model on composite instructions, we conduct human
evaluations since there is no standard test dataset available. We evaluate the
model’s generalizability to new task combinations by selecting new composite
instructions than the ones in the training set. We conduct two sets of
pairwise annotations (similar setup as the one in Section 6.1) comparing
CoEdIT-xl-C with GPT3-Edit and CoEdIT-xl.

For a fair comparison against CoEdIT-xl, we prepare a chaining
pipeline151515Chaining increases the inference time, and the ordering of the
tasks in the sequence is also likely to result in different outputs. We leave
the optimal ordering of the tasks in prompt chaining for future work. by
decomposing composite instructions into multiple simple instructions and
executing them sequentially. In the case of GPT3-Edit, we compare its
capability to handle composite instructions similar to CoEdIT-xl-C.

Table 8 shows the results of the human evaluation comparing the performance of
CoEdIT-xl-C on composite instructions with GPT3-Edit and CoEdIT-xl. In 38% of
cases, the annotators show a preference for the outputs generated by CoEdIT-
xl-C, as opposed to 34% for GPT3-Edit. In 3% cases, both models are preferred
equally, whereas, for 25% of the inputs, both models generate unacceptable
predictions. When comparing the performance of CoEdIT-xl-C with CoEdIT-xl
(i.e. chaining baseline), we observe a similar pattern. The annotators prefer
CoEdIT-xl-C’s output for 34% of the inputs, compared to 21% for the CoEdIT
model. Both outputs are preferred equally in 14% cases, whereas, for 31% of
the inputs, both models generate unacceptable predictions. Table 9 provides a
side-by-side comparison of the outputs generated by the three models.

Instruction |  GPT3-Edit |  CoEdIT-xl (chaining) |  CoEdIT-xl-C
---|---|---|---
Remove all grammatical errors from this text, and make this text less complex: They follow the way the Sun goes from the east to the west. |  The Sun rises in the east and sets in the west. |  They move with the sun as it goes from east to west. |  They follow the sun as it goes from east to west.
Make the sentence grammatical, rewrite the sentence with different wording, and make this text less complex: Fives is a British sport believed to derive from the same origins as many racquet sports. |  Fives is a British sport that is believed to derive from the same origins as many racquet sports. |  It is thought to have come from the same sources as other racquet sports. |  Fives is a British sport that is believed to have originated from racquet sports.
Make the text coherent, and rewrite this text: Nebulous is initially unconcerned when he is unable to finish his cheese sandwich. He soon discovers he is dealing with an infinite sandwich and immeasurable garnish. |  Nebulous is initially unconcerned when he is unable to finish his cheese sandwich. He soon discovers he is dealing with an infinite sandwich and immeasurable garnish. He is then unable to finish his cheese sandwich. |  Nebulous wasn’t too worried about the fact that he couldn’t finish his cheese sandwich, but soon realized that this was an endless sandwich and an endless amount of condiments. |  Nebulous is initially unconcerned when he is unable to finish his cheese sandwich, but soon realizes he is dealing with an endless sandwich and an endless supply of condiments.
Make the text more coherent, simplification, and rewrite this sentence: Phantom Budo is an incredibly powerful ability. Spike has yet to fully develop this awesome fighting technique. |  Phantom Budo is an incredibly powerful ability. It is an awesome fighting technique that Spike has yet to fully develop. |  This fighting technique of Spike is still in development. |  Spike has yet to fully develop his Phantom Budo ability.
Rewrite the sentence to be simpler, and paraphrase this text: The jury asked for transcripts of Quattrone’s testimony about his role in the IPO allocation process. |  The jury asked for transcripts of Frank Quattrone’s testimony about his role in the initial public offering allocation process. |  The jury requested a copy of Quattrone’s testimony. |  Transcriptions of Quattrone’s testimony were requested by the jury.
Rewrite more formally, and make this easier to understand: brian, he asked the coolest questions, they suspended him though, i dont know what’s up with that! |  Brian, he asked the coolest questions, they suspended him though, I don’t know what’s up with that! He was a cool guy, and I liked his questions. |  I do not understand why Brian was suspended. He had some great questions. |  I do not know why they suspended Brian.
Table 9:  A sample of text revisions generated by GPT3-Edit, CoEdIT-xl
(chaining) and CoEdIT-xl-C for composite instructions.

##  7 Conclusion

We introduce CoEdIT – a fully open-sourced dataset and set of instruction-
tuned large language models that can act as a writing assistant by following
natural language instructions to perform various textual edits by removing,
updating, or adding words, phrases, and sentences. CoEdIT achieves state-of-
the-art performance on multiple text editing benchmarks, spanning syntactic,
semantic, and stylistic edit requirements. Through extensive experiments, we
have shown that CoEdIT is capable of further generalizing to unseen and
composite instructions to perform edits along multiple dimensions in a single
turn. In our human evaluation, we observe that CoEdIT can assist writers with
various aspects of the text revision process at scale by following natural
language instructions.

## Limitations

Although CoEdIT achieves state-of-the-art performance on multiple text editing
benchmarks, we acknowledge some limitations to our approach and evaluation
methods. Our task-specific fine-tuning (like most other works) mainly focuses
on sentence-level editing tasks, and its effectiveness on much longer
sequences of texts that are more appropriate to a real-world editing settings
remains to be seen. Additionally, our system mainly focuses on non-meaning-
changing text edits, thus, which could potentially limit the utility of our
model to more real-world scenarios where fact-based editing or corrections are
needed. Another limitation of our work involves prompt sensitivity. While we
construct our inputs by randomly choosing from a pool of verbalizers for every
task, we acknowledge that different prompts may induce better or worse edits,
and as we evaluate each input with a random verbalizer, a fully controlled
comparison for each available prompt across all models is not done. We plan to
address this in future work. Additionally, computing resource requirements
could pose some difficulty in replicating the results (which we try to address
by sharing our models publicly).

## Ethics Statement

Since our work mainly focuses on non-meaning-changing text edits, we are able
to avoid many issues involving generating harmful text. Although, there is
still a possibility of small meaning changes for stylistic tasks, we try to
reduce the chance of hallucinations by constraining the generation to strictly
edit tasks in order to reduce the chance of adding any new information, or
perpetuating biases.

## Acknowledgements

We thank Alice Kaiser-Schatzlein, Robyn Perry, Maya Barzilai and Claudia
Leacock for providing their invaluable linguistic expertise and insightful
feedback with the evaluations. We also thank Max Gubin, Leonardo Neves, and
Vivek Kulkarni for their helpful feedback.

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##  Appendix A Training Dataset Description

In this section, we discuss the details of the datasets used to create our
training datasets and also expand on the dataset creation pipeline. For both
CoEdIT and CoEdIT-Composite, we use the following datasets:

Fluency: We use three prominent corpora for GEC: the NUS Corpus of Learner
English (NUCLE) Dahlmeier et al. (2013), the W&I-locness Bryant et al. (2019),
and the NAIST Lang-8 Corpus of Learner English Tajiri et al. (2012), which is
one of the largest and most widely used datasets for GEC.

Clarity: We split Clarity into two sub-tasks, one focused on Text
Simplification, and the other category focused on the set of edits outside of
Simplification. In total, we use five corpora for Clarity tasks: Four of them
- the Newsela corpus Xu et al. (2015), WikiLarge Zhu et al. (2010); Woodsend
and Lapata (2011); Kauchak (2013), WikiAuto Jiang et al. (2020) and a subset
from ParabankV2 corpus Hu et al. (2019) focus on text simplification, and the
last one comes from the Clarity split of IteraTeR Du et al. (2022b).

Coherence: We use the DiscoFuse dataset Geva et al. (2019), as it involves
linking two given sentences as coherently as possible using edit operations
such as inserting discourse connectives.

Style: Owing to the subjective nature of style edits based on different sub-
intentions (eg. conveying writers’ writing preferences, including emotions,
tone, and voice, etc.). We use the following datasets for making different
stylistic edits to reflect those distinctions:

  * •

Formality: We use Grammarly’s Yahoo Answers Formality Corpus (GYAFC) Rao and
Tetreault (2018) which is a parallel corpus of informal and formal sentence
pairs from two different domains.

  * •

Neutralization: We use WNC Pryzant et al. (2020), a dataset from the
Subjective Bias Neutralization task, where the objective is to remove or
mitigate biased words to make sentences more neutral;

  * •

Paraphrasing: For paraphrase generation, we used the ParabankV2 corpus Hu et
al. (2019), since it is a large-scale corpus that contains multiple diverse
sentential paraphrases.

Once the raw datasets were collected, we randomly sampled them to the
quantities mentioned in Table 1 based on a few heuristics such as old word
retention, complexity ratios, dependency tree depth ratio, and character
length ratio. The sampled pairs were then modified by prefixing the source
texts with task-specific verbalizers (Appendix B) to convert a <source,
target> pair to a <instruction: source, target> pair. All our models were then
fine-tuned on the verbalized dataset.

Composite instructions: Table 10 shows the composition of the CoEdIT-Composite
dataset, in addition to the details about datasets and prompts. We use seven
such composite instructions during model training. These are GEC-Paraphrasing,
GEC-Simplification, GEC-Paraphrasing-Simplification, Formality-Paraphrasing,
Formality-Simplification, Formality-Paraphrasing-Simplification, and
Paraphrasing-Simplification. For the first three composite prompts, we use GEC
datasets to extract datapoints that show simplification and paraphrasing edits
in addition to GEC. For the next three prompts, we use the formality dataset
(GYAFC) to extract pairs which exhibit paraphrasing and simplification edits
in addition to formality. Lastly, for the last prompt, we use the ParabankV2
paraphrasing dataset to extract data points which show a simplification of the
source text in addition to paraphrasing.

To select the appropriate source-target pairs for a composite instruction, we
use similar heuristics as with single-task instructions, i.e. old word
retention, complexity ratios, dependency tree depth ratio, and character
length ratio. For example, a source-target pair from a GEC dataset can be used
for the composite instruction involving GEC, paraphrasing and simplification
if the target and source sentence has a high edit distance and low complexity
ratio, character length and word retention scores. The exact details can be
found in the code.

For human evaluation, we use three new composite instructions in addition to
the seven composite instructions that were used during training. These are
Coherence-Paraphrase, Coherence-Simplify, and Coherence-Simplify-Paraphrase.

Finally, for building the prompts for the composite instructions, we randomly
sample from the task-specific verbalizers and concatenate them. The ordering
of the single tasks in a composite instruction is also chosen randomly to
ensure better generalization.

Edit Intention |  Datasets |  Size |  Example Input |  Example Output
---|---|---|---|---
GEC-Paraphrase |  NUCLE-14 Lang-8 BEA-19 |  $1k$ |  Fix grammar in this sentence, and rewrite this sentence: How about taking account of psychology? |  One such perspective is to take psychology into account.
GEC-Simplify |  NUCLE-14 Lang-8 BEA-19 |  $1k$ |  Make this easier to understand, and remove grammatical mistakes: So it was not like enjoying the tasteful gardens. |  So it was not as if I could enjoy the pretty gardens.
GEC-Paraphrase-Simplify |  NUCLE-14 Lang-8 BEA-19 |  $1k$ |  Rewrite this sentence, change to simpler wording, and fix the grammar mistakes: Due to ageing, some of the people may suffer from physical and mental depreciation. |  Due to the effects of aging, some people may suffer.
Formality-Paraphrase |  GYAFC |  $5k$ |  Make this sound more formal, and paraphrase: writers dont think about what they will write, they just write!!! |  Some writers can write freely without putting too much thought to it.
Formality-Simplify |  GYAFC |  $2k$ |  Rewrite more formally, and make this text less complex: Not to my knowledge…I’m a little curious myself now though. |  I do not think so.
Formality-Paraphrase-Simplify |  GYAFC |  $4k$ |  Rewrite the sentence to be simpler, make this sound more formal, and paraphrase this sentence: my answer is what…very clever riddle!! |  Your riddle was very clever, and I am unsure how to respond.
Paraphrase-Simplify |  ParabankV2 |  $5k$ |  Use simpler wording, and write a paraphrased version of the sentence: In your second communication, you requested reinforcements. |  You asked for backup in your second report
Table 10: Example data instances with composite instructions in the CoEdIT-
Composite dataset (90K <instruction: source, target> pairs). Instructional
prompts in the inputs are italicized.

##  Appendix B Task Verbalizers

We manually curated a variety of task-specific verbalizers to construct the
instructional inputs. Table 11 shows the full list of the verbalizers used for
training and evaluations. Table 12 shows the verbalizers used for the
experiments conducted in Section 6.2.

Edit Intention / Task |  Verbalizers
---|---
GEC |  Fix grammar, Fix grammar in this sentence, Fix grammar in the sentence, Fix grammar errors, Fix grammatical errors, Fix grammaticality, Fix all grammatical errors, Fix grammatical errors in this sentence, Fix grammar errors in this sentence, Fix grammatical mistakes in this sentence, Fix grammaticality in this sentence, Fix grammaticality of the sentence, Fix disfluencies in the sentence, Make the sentence grammatical, Make the sentence fluent, Fix errors in this text, Update to remove grammar errors, Remove all grammatical errors from this text, Improve the grammar of this text, Improve the grammaticality, Improve the grammaticality of this text, Improve the grammaticality of this sentence, Grammar improvements, Remove grammar mistakes, Remove grammatical mistakes, Fix the grammar mistakes, Fix grammatical mistakes
Clarity |  Clarify the sentence, Clarify this sentence, Clarify this text, Write a clearer version for the sentence, Write a clarified version of the sentence, Write a readable version of the sentence, Write a better readable version of the sentence, Rewrite the sentence more clearly, Rewrite this sentence clearly, Rewrite this sentence for clarity, Rewrite this sentence for readability, Improve this sentence for readability, Make this sentence better readable, Make this sentence more readable, Make this sentence readable, Make the sentence clear, Make the sentence clearer, Clarify, Make the text more understandable, Make this easier to read, Clarification, Change to clearer wording, Clarify this paragraph, Use clearer wording
Simplification |  Simplify the sentence, Simplify this sentence, Simplify this text, Write a simpler version for the sentence, Rewrite the sentence to be simpler, Rewrite this sentence in a simpler manner, Rewrite this sentence for simplicity, Rewrite this with simpler wording, Make the sentence simple, Make the sentence simpler, Make this text less complex, Make this simpler, Simplify, Simplification, Change to simpler wording, Simplify this paragraph, Simplify this text, Use simpler wording, Make this easier to understand
Coherence |  Fix coherence, Fix coherence in this sentence, Fix coherence in the sentence, Fix coherence in this text, Fix coherence in the text, Fix coherence errors, Fix sentence flow, Fix sentence transition, Fix coherence errors in this sentence, Fix coherence mistakes in this sentence, Fix coherence in this sentence, Fix coherence of the sentence, Fix lack of coherence in the sentence, Make the text more coherent, Make the text coherent, Make the text more cohesive, logically linked and consistent as a whole, Make the text more cohesive, Improve the cohesiveness of the text, Make the text more logical, Make the text more consistent, Improve the consistency of the text, Make the text clearer, Improve the coherence of the text
Formality Style Transfer |  Formalize, Improve formality, Formalize the sentence, Formalize this sentence, Formalize the text, Formalize this text, Make this formal, Make this more formal, Make this sound more formal, Make the sentence formal, Make the sentence more formal, Make the sentence sound more formal, Write more formally, Write less informally, Rewrite more formally, Write this more formally, Rewrite this more formally, Write in a formal manner, Write in a more formal manner, Rewrite in a more formal manner
Neutralization |  Remove POV, Remove POVs, Remove POV in this text, Remove POVs in this text, Neutralize this text, Neutralize the text, Neutralize this sentence, Neutralize the sentence, Make this more neutral, Make this text more neutral, Make this sentence more neutral, Make this paragraph more neutral, Remove unsourced opinions, Remove unsourced opinions from this text, Remove non-neutral POVs, Remove non-neutral POV, Remove non-neutral points of view, Remove points of view, Make this text less biased
Paraphrasing |  Paraphrase the sentence, Paraphrase this sentence, Paraphrase this text, Paraphrase, Write a paraphrase for the sentence, Write a paraphrased version of the sentence, Rewrite the sentence with different wording, Use different wording, Rewrite this sentence, Reword this sentence, Rephrase this sentence, Rewrite this text, Reword this text, Rephrase this text
Table 11: Complete list of task-specific verbalizers used in our training and test datasets. Edit Intention / Task |  Verbalizers
---|---
Sentence Compression |  Shorten the sentence, Shorten this sentence, Compress this sentence, Shorten this text, Compress this text, Write a shorter version for the sentence, Rewrite the sentence to be shorter, Rewrite this sentence in a shorter manner, Rewrite this sentence for shorter length, Make the sentence short, Make the sentence shorter, Make this shorter, Shorten, Compress, Shorten this paragraph, Shorten this text
Politeness |  Increase politeness, Make this polite, Make this more polite, Make this sound more polite, Make the sentence polite, Make the sentence more polite, Make the sentence sound more polite, Write more politely, Rewrite more politely, Write this more politely, Rewrite this more politely, Write in a polite manner, Write in a more polite manner, Rewrite in a more polite manner
Table 12: List of task-specific verbalizers used for generalizability experiments. |  Model |  Size | IteraTeR |  Clarity |  Coherence | Style (Paraphrasing)
---|---|---|---|---|---|---
|  |  IteraTeR-FLU↑ |  IteraTeR-CLA↑ |  IteraTeR-COH↑ |  TURK↑ |  DiscoFuse-Sport↑ |  STS(↓/↑) |  QQP(↓/↑)
(a) |  Copy |  - |  31.9 |  28.6 |  30.8 |  26.3 |  30.5 |  39.6 / 100 | 30.1 / 100
|  T5-large |  770M |  15.1 |  23.8 |  21.7 |  34.2 |  28.4 |  18.0 / 50.5 | 18.2 / 62.5
|  T0* |  3B |  27.1 |  28.0 |  21.2 |  34.8 |  33.6 |  27.7 / 88.7 | 10.2 / 63.4
(b) |  Tk-Instruct* |  3B |  22.3 |  20.9 |  21.2 |  32.3 |  29.1 |  12.3 / 49.5 | 19.1 / 79.6
|  T0++* |  11B |  37.9 |  30.2 |  27.5 |  34.1 |  35.1 |  29.9 / 99.0 | 16.6 / 79.1
|  BLOOM |  7B |  35.8 |  29.7 |  23.7 |  28.3 |  31.7 |  1.5 / 22.7 | 1.9 / 52.4
(c) |  LLaMA |  7B |  32.4 |  28.8 |  31.4 |  27.2 |  30.8 |  1.4 / 14.4 | 1.5 / 35.8
|  GPT3 |  175B |  20.9 |  21.8 |  21.2 |  34.6 |  27.0 |  0 / 8.2 | 0 / 13.1
|  Alpaca |  7B |  33.0 |  28.9 |  31.0 |  27.4 |  30.8 |  0 / 25.8 | 0 / 64.4
(d) |  ChatGPT |  - |  36.4 |  23.1 |  31.4 |  37.4 |  39.1 |  7.9 / 96.9 | 8.17 / 96.5
|  InstructGPT |  175B |  43.7 |  28.1 |  33.9 |  38.9 |  45.8 |  11.6 / 88.7 | 9.3 / 95.5
|  GPT3-Edit |  176B |  48.3 |  31.8 |  34.2 |  34.7 |  48.0 |  14.0 / 86.6 | 6.4 / 94.5
|  BLOOM |  7B |  32.6 |  28.7 |  31.0 |  26.8 |  30.9 |  0 / 69.1 | 0 / 54.1
|  Alpaca |  7B |  33.0 |  28.9 |  31.0 |  28.4 |  32.1 |  0 / 39.2 | 0 / 63.4
(e) |  GPT3 |  175B |  33.9 |  30.8 |  33.9 |  38.7 |  46.4 |  0 / 2.1 | 0 / 5.9
|  ChatGPT |  - |  36.2 |  27.0 |  35.6 |  37.3 |  49.4 |  0 / 88.7 | 0 / 96.8
|  InstructGPT |  175B |  44.7 |  31.7 |  37.5 |  39.5 |  56.9 |  0 / 91.8 | 0 / 96.1
|  IteraTeR |  570M |  36.7 |  30.4 |  34.7 |  27.2 |  40.9 |  24.1 / 81.4 | 20.6 / 94.3
(f) |  DelIteraTeR |  570M |  32.0 |  28.9 |  30.6 |  27.8 |  31.7 |  24.7 / 87.6 | 20.4 / 96.6
|  PEER-3B* |  3B |  51.4 |  32.1 |  32.1 |  32.5 |  - |  - | -
|  PEER-11B* |  11B |  52.1 |  32.5 |  32.7 |  34.1 |  - |  - | -
|  CoEdIT-l |  770M |  46.8 |  30.9 |  31.5 |  38.5 |  70.5 |  22.4 / 92.8 | 15.7 / 97.2
(g) |  CoEdIT-xl |  3B |  50.4 |  31.3 |  31.5 |  38.5 |  74.6 |  20.8 / 94.8 | 15.4 / 97.8
|  CoEdIT-xxl |  11B |  51.6 |  31.8 |  31.5 |  38.2 |  76.2 |  21.8 / 93.8 | 15.4 / 98.2
Table 13:  Comparison of CoEdIT against various baselines (on sub-tasks and
additional datasets to Table 2), divided into seven groups: (a) a copy
baseline and T5-large baseline prefixed only with task-specific tags (i.e.
<gec>, <clarity>, etc.) (b) T5-based models, (c) Decoder-only LLMs, (d)
Instruction-tuned LLMs, (e) Prompt-tuned versions of large pre-trained models
(f) SOTA text editing models, and, (g) Variants of CoEdIT models (our work).
The first scores for each task (excluding STS and QQP style tasks) are SARI
scores. For STS and QQP. the first scores are Self-BLEU scores, and the second
is semantic similarity. ($\uparrow$) indicates higher is better, and
($\downarrow$) indicates lower is better. The best-performing models for each
dataset are highlighted in bolded boxes.

##  Appendix C Training Details

We used the Adam optimizer with a learning rate of $1e-4$. Each model is
trained for 5 epochs with early stopping. All models were fine-tuned on A100
GPUs using Deepspeed Rasley et al. (2020). Maximum sequence lengths for both
the source and the target were set to 256 tokens (via filtering). The best-
performing checkpoints were chosen based the validation loss.

##  Appendix D Model Performance

Table 13 compares the performance of the CoEdIT with the other models on the
remaining test datasets. We observe similar trends as the ones observed in
Table 2, where CoEdIT outperforms most models we compare against.