CoEdIT: Text Editing by Task-Specific Instruction Tuning
Authors: Vipul Raheja
Dhruv Kumar
Ryan Koo
Dongyeop Kang
Venue: EMNLP 2023 Findings (2023)
Keywords: Text Editing
Instruction Tuning
Large Language Models
TL;DR: CoEdIT: A state-of-the-art instruction-tuned text editing model with smaller size, high generalization, and preferred edits by linguistic experts over larger models.

Abstract: We introduce CoEdIT, a state-of-the-art text editing system 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 ∼60x smaller, (3) is capable of generalizing to unseen edit instructions, and (4) exhibits abilities to generalize to composite 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, data, and models are publicly available at https://github.com/vipulraheja/coedit.

COEDIT: Text Editing by Task-Specific Instruction Tuning
Vipul Raheja
Dhruv Kumar" Grammarly first. ramarrarramal....mmmmmm com
Ryan Koo
Dongyeop Kang"
University of Minnesota {koo00017, dongyeopJ@umn.ce edu
Abstract
60
CoESTT (Qarg)
55-
15-lbwad
We introduce COEDIT, a state-of-thh-ans text editing system for writing assistance. COEDIT takes instructions from the user specifying the attributes of the desired text, such "Make the sentence simpler" or "Write it in a more neutral style," and outputs the edited text. We present large language model fine-tuned diverse collection of task-specific instructions for text editing total of 82K instructions). Our model (I) achieves state-of-the-ar perfor- mance on various text editing benchmarks, (2) is competitive with publicly available largest- sized LLMs trained on instructions while being ~60x smaller, (3) is capable of generalizing to unseen edit instructions, and (4) exhibits abili- ties to generalize to composite instructions con- taining 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'.
50-
Overall Performancs
Cutalnil
45
Prempami
InstrccGPT
40-
35
Itero Tea
Alpoca
To++
30-
CP13
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Figure I: Model comparison according to training pa- rameters vs. average performance across all text editing benchmarks reported Tables
Publicly avail-
able models denoted with (*).
Text editing is a complex task because human writers cannot simultaneously grasp multiple de- mands and constraints of the task and tend to iterate and revise their work multiple times (Flower, 1980; Collins and Gentner, 1980; Vaughan and McDon- ald, 1986). This poses significant challenge for intelligent writing assistants.
1 Introduction
Large language models (LLMs) have made remark- able progress toward generating coherent text in wide variety of tasks and domains support writ- ing assistance (Du et al., 2022a; Mallinson al.. 2022; Schick al., 2023), such as grammatical er- ror 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 ca- pability to generalize to unseen tasks by following composed instructions. instruction-tuning where LLMs fine-tuned on 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).
In this work, we aim to improve the capabili- ties of instruction-tuned models for text editing by leveraging instruction-tuming from diverse tasks of text editing benchmarks. While multiple previous works have attempted to develop general-purpose text editing models using LLMs, they either not trained with instruction-tuning (Du 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.
We introduce COEDIT, text editing system de- signed provide writing assistance with natural language interface. A user can employ COEDIT by providing natural language instructions such "Paraphrase the sentence" or "Fix the grammar". Our experiments demonstrate that fine-tuning in-
'Code, data, and models available at https: /ttps://gti thub. com/viipulrahejjccedii

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Maki toxt polite
Poraphrase this
quence labeling, or are not general enough 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 (Stajner et al., 2022), paraphrase generation (Chowdhury et al.,
Summarize toxt
Neutralize
Make toxt coherent
Transiate French
Make renduble and coherent
Rewrtte easier understond
Sporse task distribution
Dense task distribttion Generafize composiie
Unssennosks
2022), or style transfer (Reif et al., 2022), which limits their generalizability.
Figure 2: General-purpose (left) vs Task-specific (right) Instruction Tuning.
Instruction Tuning for Writing Assistance Ex- plicitly teaching models how to follow natural language instructions is closely related to recent work for fine-tuning models using large datasets of human-written instructions (Wei et al., 2022; Mishra et al., 2022; Sanh et al., 2022; Ouyang al., 2022a: Wang al., 2022; Iyer et al., 2022; Bach et al., 2022; Longpre et al., 2023). Recently, ad- vanced 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 perfor- mance, suggesting instruction-tuned models offer more cnapuutationally-efffiiiii starting check- point for downstream applications, corroborating Liu et al. (2022) and Aribandi et al. (2022).
structions for specific tasks more effective than multi-task learning and general- general-purpose purpose instruction tuning. We conjecture that task-specific instruc- tions increase the density of the instruction space, reinforcing the complementary effects of multiple tasks and facilitating their generalization to com- posite and new text editing tasks, shown in Fig. 2.
To build COEDIT, we fine-tune a pre-trained sequence-to-sequenee model on a parallel corpus of instruction- based 82K input-output pairs. The inputs and outputs are sourced from publicly avail- able corpora for different text editing tasks, and the instructions are constructed based on rules that introduce lexical and semantic variations.
Our main contributions are follows:
We achieve state-of- of-the-art performance on mul- tiple text editing tasks: grammatical error cor- rection, text simplification, sentence fusion, it- erative text editing, and three stylistic editing tasks (formality style transfer, neutralization, and paraphrasing).
On instruction tuning for writing assistance, our work is closely related to PEER (Schick et al.. 2023), who fine-tuned T5-based LLMs by follow- ing user-provided text-editing plans to perform the said edits. There are few significant differences in our approach compared PEER While PEER at- tempts to either create or leverage a user-proviin usev-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 editing plans in the form of natural lan- guage instructions, our work focuses on edits that do not add new information. Therefore, pare our models only against PEER-Edit models.
We find that even our smallest instruction-tunel model outperforms other supervised text editing models, instruction-ture tundd models, and general- purpose LLMs with nearly 60x greater parame- ters, on both manual and automatic evaluations. COEDIT generalizes well to new, adjacent tasks not seen while finettnnig, as well as composite instructions with multiple task specifications. Our data and models will be publicly available.
com-
Finally, no prior works, to the best of our knowl- edge, have investigated the ability of instruction- tuned LLMs for text editing to generalize to com- posite instructions.
2 Related Work
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 al., 2021: Reid and Neubig, 2022; Mallinson et al., 2022: Du et al., 2022a,b; Kim al., 2022). However, these aforementioned works are either not based on instruction tuning, use dif- ferent modeling techniques such as tag-baset se-
3 COEDIT
3.1 Training Dataset
Our dataset creation is based on the ITERATER+ dataset proposed by Kim et al. (2022) who com- bined datasets from various text editing tasks (Sec Table 1). Their work, in turn, is based on Du et al.

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Edit Intention
Datasets
Size
Example Input
Example Output
FLUENCY
NUCLE-14, Lang-8.
20k
Fix the grammar: When I grow up. start understand what he said quite right.
When grew started to understand what he quite right.
BEA-19
COHERENCE
DiscoFuse
11k
Make this 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
NEWSELA WikkAAuo, WikiLarge,
13k Rewvite to make this egsier to understand: storm surge is what forecasters consider burricane's most treacherous aspect.
A storm surge considered hurricane's most dangerous aspect.
(Simplification)
ParabankV2
ITERATER-CLARIE CLARITY
STYLE
Pa
15k Paraphrase this: Do you know where was born??
Do you know my birthplace?
(Paraphrasc)
STYLE
GYAPC
12k
Write this more formally: omg love that song im listening to right now
I love that song and am listening to it this moment.
(Furmalizze
STYLE
WNC
11k Write
nentral way: The authors' exposé nutrition studies.
The authors' staterents nutrition stud- ies.
(Neutralize)
Table I: Example data instances in the COEDIT dataset. Instructions in the inputs are italicized.
(2022b), who categorized each edit into MEANING- CHANGED KON-MEANING-CHANGEE that belong to the latter group further assigned FLUENCY, COHERENCE, CLARITY, or STYLE. The aforementioned taxonomy of edit intents from ITERATER reflects writers' general intention be- hind their revision, providing more in-depth infor- mation than just superficial edit operations, such as ADD and DELETE.
domly sample instruction from the aforemen- tioned group of task-specific instruction candidates to be pre-pended to the source in order form <instruction: source, target> data pair. We provide the full list of our instructional prompts &C. In total, our training dataset consists of around 82K <instruction: source, target> pairs. We keep the original train seii---aiiaaiio-ttee splits con- sistent as the original datasets but diversify the train and validation splits with the paraphrasing augmen- tations. The details of datasets and instructions used to train our models are described in $A.
Edits
Similar to Kim et al. (2022), our work focuses on non MEANIIGGCHHAAANEE
edits. We consider those edits to be ones that do not add new infor- mation perform fact updates. Since the STYLE edits are quite subjective in nature, allow for the possibility of meaning change so as fulfill the needs of making stylistic edits, but we constrain the editing tasks to ensure the edited texts are se- mantically similar to the sources, but not to the extent of adding new information or fact updates. With this in mind, we expand the STYLE edit in- tention category from ITERATER+ to include three sub-intentions: Paraphrasing, Formality Style Transfer (or Formalization), and Neutralization.
3.2 Text Editing Models
We fine-tune different versions of pre-trained FLANTS (Chung et al., 2022a) models on the COEDIT dataset. Specifically, we use FLANTS-L (770M parameters), FLANT5-XL (3B parameters), FLANT55 (11B parameters) models, which are henceforth referred to as COEDIT-L, COEDIT-XL. and COEDIT-XXI. respectively. The training de- tails are summarized in $D.
The aforementioned ITERATER dataset taxon- omy lends itself conveniently to be articulated as natural language instructions and allows us nat- urally 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 tem- plates, e.g., instead of "Write" we also use "Gen- erate" "Rewrite." " or instead of "Paraphrase the texi" we use "Rewrite the text with different word- ing." " and so on. For each task, we develop a vari- ety such diverse instructional prompts and ran-
Experimental Setup
We conduct experiments to determine if a stan- dard instruction-tund language model fine-tuned using task-specific data can improve text editing performance and if it can further generalize into general-purpose text editing model capable of following human-wiin written instructions and handling a wider array of editing tasks, such as unseen and composite instructions. Specifically, we aim to answer the following research questions:
RQ1: Can COEDIT follow text editing instruc- tions and perform high-quality edits across a wide variety of tasks?

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RQ2:
Is COEDIT generalizable to perform
Alpaca (Taori et al., 2023) is an instruction- tuned version of the LLaMA-7B model (Tou- vron et al., 2023) trained on 52K instruction- following demonstrations generated by GPT3. We also compare InstructGPT (Ouyang et al., 2022a), a variant of GPT3 fine-tuned via rein- forcement learning on large dataset of instruc- tions and human- outputs."
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 analy- ses of model outputs (Section 5) and via qualitative analyses and human evaluations of model outputs (Section 6). Further, investigate RQ2 along two dimensions: (1) generalization to composite instructions containing combinations of multiple different kinds of edits and (2) out-of-domain gener- alization to instructions with new task requirements on previously unseen data.
GPT3.5 (henceforth referred to as ChatGPT), is an improved version of InstructGPT opti- mized for chat. We utilize OpenAT's API for all inference tasks.'
GPT3 also offers a text Editing API (we refer to as GPT3-Edit), which is usable for editing tasks rather than completion, making it directly comparable to the tasks we train COEDIT on.
4.1 Models
No-Edits Baseline We first evaluate a no-edits baseline, where the ouiput is simply a copy of the source input without the instruction. This strategy performs reasonably well on tasks where the target output largely overlaps with the input (e.g., GEC).
Large-Pretrained Decoder-only Models We compare against LLMs with no instruction tuning in two settings zero-shot and few-shot (details in Section 5.1):
The 175B GPT3 (Brown et al., 2020) model that not instruction-tuned demonstrates strong general-purpose text revision capabilities.
Supervised Text Editing Models We also evalu- ate existing LLMs for text editing that are not fine- tumed with instruction-specine data. Specifically, to understand the effect of task-specific fine-tuning, we evaluate against T5 (Raffel et al., 2020b) mod- els as primary alternatives of our FLAN-T5 models. models against ITERATER (Du et al., 2022b) and DELITERATER (Kim et al., 2022), which have shown strong performance on variety of text editing tasks." 3
LLaMA (Touvron et al., 2023) is Meta Al's general-purpose language model trained only on publicly available data. We utilize the 7B model due to computing constraints.
We also compare
Outputs of all models were generated using greedy decoding unless specified otherwise.
4.2
Test Datasets
To assess the editing capabilities of COEDIT, we perform evaluations on standard test sets sourced from a 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 evaluation datasets and evaluation metrics as close theirs possible for consistency: We used JFLEG (Napoles al., 2017) for grammati- cal error collection, TurkCorpus (Xu et al., 2016) and ASSET (Alva-Manchego et al., 2020) for text simplification, Coherence split of ITERATER (Du et al., 2022b) and the DISCOFUSE dataset (Geva et al., 2019) for coherence, and ITERATER (Du et al., 2022b) for iterative text revision. For Style- related edits, used GYAFC (Rao and Tetreault, 2018) for formality style, WNC (Pryzant al., 2020) for neutralization, and MRPC (Dolan and
Instruction-tumed LLMs A major group of our comparisons is against Instruction-tuncd LLMs: Our main comparison is against PEER (Schick et al., 2023), which is primarily based on the LM Adapted variant of T5. As the focus of our work is improving revision quality (Section 2), we compare against FEER-EDIT (both 3B and 11B versions).
To, T0++ (Sanh et al., 2022) and Tk-Instruct (Wang et al., 2022), which are all initialized from the LM Adapted variant of T5, and fine- tuned using PromptSource (Bach et al., 2022), and Super- Naturrraattrllnnstuutttonnsss (Wang et al., 2022) datasets, respectively.
'The original T5 model cannot continue text well due to its infilling pre-training objective. Hence, similar to Schick al. (2023), evaluate LM Adapted versions (Lester al., 202 which trained with language modeling objective. "Wo 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.
"We use text-davinci-003 We gpt-3. 5-5tbb "We use exxt-davinci-edi--a

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Brockett, 2005), STS (Cer al., 2017), and QQP for paraphrasing. Detailed descriptions of each dataset and its evaluation metries are in $B.
Next, in the fifth group (e), we evaluate the LLMs under a few-shot setting. As mentioned in Section 4.1, we conduct these experiments a 4-shot evaluation setting, where example inputs were constructed by randomly sampling four inputs for each task from the COEDIT dataset such that all examples chosen would fit in the input window for all models as seen in (Brown et al., 2020). The input sentence and its corresponding revised refer- ence were pre-pended to the instructional prompt. We conduct few-shot evaluations for decoder-only LLMs (GPT3) and three instruction-tuned LLMs (InstructGPT, ChatGPT, and Alpaca). Outputs of all models were generated using greedy decoding unless specified otherwise.
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. 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 11.
We segregate the models into seven groups. The first group (a) consists of the copy baseline and T5-LARGE baseline fine-tuned with prefix-tuning (each data point is prefixed with task-specific tags rather than instructions), while the second group (b) consists of instruction-finer fine-tuned 15-based models on non-text-editing tasks. We find that COEDIT substantially outperforms these models across all tasks.
We observe that giving specific examples im- proves performance in all models for all tasks ex- cept MRPC for GPT3. This may be because GPT3 still exhibits some similar behavior in repeating its generations continuously, resulting in a low BLEU score but low semantic similarity as well. We don't present any experiments for GPT3-Edit under the few-shot setting, as scores tended to stay the same actoss all tasks implying that GPT3- may not have good in-context learning capabilities. Over- all, we find that even our smallest 770M parameter model is competitive against LLMs evaluated in a few-shot setting in most tasks.
The next two groups (c, d) show different LLMs varying from 7B to 176B parameters in size, evalu- ated in zero-shot setting. Those in group (c) are decoder-only models, while those in group (d) are instruction-tunet We find that COEDIT outper- forms all LLMs comparable to its model size (e.g.. Al Alpaca and LLaMA) across all tasks, as well as on most tasks compared to models several times larger, such as ChatGPT and InstructGPT. This indicates that current general- purposs and instruction-tuned models are underfitted, and it is beneficial to den- sify the task/instruction space rather than to scale model size.
In the final group (f), we compare our models against task-specific text editing models such ITERATER, DELITERATER, and PEER. ITER- ATER 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 ITERATEE and DELITERATTE were trained with task-specific tags. Furthermore, they were trained using BART and Pegasus, respectively, both of which have a summarization pre-training objective, and were not trained to follow instructions. 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 uses Wikipedia as the source of instructional edit data.
Although models such as Alpaca and T5-based models (Tk-instruct, TO, TO++) have previously shown strong capabilities for zero-shot tasks, they show weaker performance compared to COEDIT. We also see that the decoder-only models (c.g.. GPT3 and LLaMA) often repeat the input for more complex tasks, such as ones under the Style intent group. This can be attributed to difficulty under- standing the prompted task, resulting in the models either repeating the input sentence or generating a continuation unrelated to the task.
5.2 Ablation Studies
"Since PEER had several scores missing. and due to the high scores of paraphrasing transfer, for fairesss, 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.
Table 3 shows the performance various baselines, which we discuss in detail in this section.
Instruction Tuning. To understand the effective- ness of instruction-tuning, we fine-tune the 3B pa-

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Model
Size
Overall
Itera
Fluency
Clarity
Coherence
Style
INERATER'
JFLE
ASSET!
DiscoFase-
GYAFC //
WNC!
MRPC
(a) COFY
27.6
29.8
26.7 40.5
20.7
30.8
17.6 10.6
3188/0
47.4/
TS-LARGB
770M
24.7
35.8
28.01
309/4.89
13/2/0
27.6.
TO
3B
29.7
26.1
42.2/ 36.1
33.2
32.4
37.9 39.3
19.470
84.
(b) TR-INSTRECT
3B
27.3
21.0
35.2/ 26.8
36.9
28.9
35.7 /3.01
24.2/0.1
20.4/48, 48.9
TU+4
118
32.6
31.5
39.4 40.5
35.1
35.5
36.8 43.7
21,2/0
42,9 94.9
(c) LLAMA
7B
28.2
30.1
2..7/33.4
21.8
31.1
18.8 19.1
31.970
5.29 74422
GPT3
1758
27.4
23.3
38.1
34.8
26
366/877 87.9
23.4/0
0/51.7
28.4
/
22.0
18.9
31.9/0
60)
GPT3-EDIT
1758
41.8
36.1
$2.4 50.6
32.9
54.0
35.7/ 52.3
/
22.6/ 98.7
INSTRUCIGPT
1758
41.6
32.6
62.4/
44.6
47.4
47.8 98.2
/..1
16.03 99.99
36.9
28.5
ALPACA (FS)
7B
30.0
30.8
33.03 11.3
23.2
33.1
20.6 95.4
32.04 /0
0.1 66,7
GPT3 (HS)
1758
38.4
50.1 /4.1
36.7/0
0/14..5
INSTRUCIGPT (PS)
45.1
55.2
99.5
CHATGPT (FS)
40.1
30.8
58 50.6
45.4
51.2
42.3/99.6
34.1/0
13.3/
ITERATER
570M
35.9/34.3
22.7/54.1
DELITRRATH
570M
28.0
29.9
21.5 31.2
21.2
32.2
18.1/578
31.9/0
39.1
PEER-3B
3B
41.7
37.1
55.5 54.3
30.5
53.3//2.6
PEER-118"
118
42.1
37.8
55.8 54.3
29.5
54.57 22.8
CoEaIT-L
770M
49.8
35.2
62.4/ 59.3
42.4
75.3
54.6798.. 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
$5.1 98.3
70.47 48.8
21.3 /
CoEaIT-XXI.
51.5
78.6
55.1/97.2
51.4
Table 2: Comparison of COEDIT against various baselines: (a) copy baseline and T5 T5-LARGE baseline with task-specific prefixes (i.e. <gec>, <clarity>, etc.) (b) TS-based models, (c) Decoder-only LLMs (zero-shot), (d) Instruction-tuma -unee LLMs (zero-shot), (e) Few-shot evaluations of pre-trained LLMs, (f) SOTA text editing models. and, (g) Variants of COEDIT models (our work). The second scores for Fluency, GYAFC, and WNC are GLEU, Formality Transfer accuracy (%), and EM. For MRPC, the first score is Self-BLEU, while the second score is semantic similarity. The best-performing models' for each dataset are highlighted in boxes. Results with (*) are ones reported prior works. (FS) denotes few-shot evaluation. Results other datasets are in Table 11.
first score for each task (excluding MRPC style task) is SARI.
IteraTeR
Flueney
Clarity
Coherence
Style
Model
Size
TreaATeR
JFLEG'
ASSET'
DicoFFaeWWul
CVARQ
WNC/'/'>
MRPO: (/)
COFDIT-XI.
3B
36.6
64.5 / 60.7
42.2
80.5
55.1 98.3
70.4 48.8
21.3 99.6
T5-XL (prefix)
3B
34.3
61.8 58.6
41.0
71.4
50.7 / 94.6
62.7 /33
30.6/
FLANT5-XI.
3B
30.2
28.3 41.3
25.5
37.9
25.0 27.8
33.5 0.0
46.6/92.5
COEDIT-XL-R
3B
33.9
63.8 60.2
36.2
69.7
52.6/
69.2
35.4/
Table Ablation results for CoEDIT evaluate the impact (a) instruction tuning (b) task-specific training, and (c) quality of instructions. The scores from left right follow exactly Table 2.
rameter T5 model (T5-XL) and compare with COEDIT-XI, its FLANT5 counterpart on the same training and validation sets. The only change is that the instructional prompts for the train- ing datasets are replaced by task-specific prefixes. Specifically, the 82k <instruction: source, target> pairs in the training dataset used to train the COEDIT models were modified to <task: source, target> We observe (Table 3(a)) that the instruction tuned COEDIT models consistently outperform prefix-tuned T5 models, showing the ef-
fectiveness of instruction-tuning over prefix-tunin -tnning
Task-Specific Training. A core contribution of
this work is to push the performance of small- (<IB 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 augmenta- tion for text editing tasks has already been shown in Kim al. (2022). For this work, we com- pare our task-specific fine-tuned models against their FLANT5 un an-tuned counterparts referred to as FLANT5 (Table 3(b)). We see a substantial gap
task was one of gec, simplify, clarify, coherence, formalize, neutralize and paraphrase

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COEDIT-XL
GPT3-Edit
Tie
Neither
Sentence Compression
Politeness Transfer
64%
10%
4%
22%
Model
SARI// CR(())
S-BLEU T*)
Table 4: Human evaluation results: Pair-wise com- parison of COEDIT-XI. against the best-performing 175B-parameter instruction-tunge LLM for text editing (GPT3-EDIT). Scores indicate the % of test inputs for which the human annotators preferred the said model.
GPT3-EDIT
23.98 / 6.09
63.31 /6.111
T5-XL (prefix)
31.47 / 7.66
81.43 / 58.82
FLANT5-XL
33.21 / 15.29
91.91 / 52.69
COEDIT-XI
35.17 22.78
60.32 64.45
Table 5: Comparison of COEDIT-XL against the best- performing con-instruction-tunee model (T5-XL), non- lask-specific-tuned model (FLANT5-XI.) and GPT3- EDIT on out-of-domain generalization.
between the two for all datasets and model sizes, thus, confirming prior findings.
Quality of Instructions. While developed with a limited set of task-specific instructional prompts, there has been widespread work on the prompt sensitivity of LLMs, especially with grow- ing model capacity (Lu et al., 2022). To assess the robustness of COEDIT models on instructional prompts, we train another baseline COEDIT-X model with randomized task task-specific instructions (henceforth referred as COEDIT-XI-RR) Specif- ically, the entire training dataset was randomized, where an instruction from one task was replaced randomly by an instruction from another task. Ta- ble 3(c) shows the results for this experiment. We observe that while COEDIT-XL-4 XL-R achieves scores that are higher than the non-task-specing tuned FLANT5-XL (especially edit-based metrics such SARI), it significantly falls behind COEDIT-XI XL on those, well as on the style accuracy met- rics such as formality transfer accuracy and para- phrasing 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 lack proper instruction tuning.
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 evalu- ations for our model outputs by linguistic experts on 50 test inputs to ensure they meet the instruc- tional constraints. Given the automatic evaluation results in Section 5, we compare our 3B-parameter COÉDIT-XL model against the largest compara- ble 175B instruction- tuned LLM for text editing GPT3-EDIT. Specifically, we conducted a pair- wise comparison: each annotator was shown an instructional input and outputs from both models (they were not aware which output was generated by which model). They were then asked to evalu- 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 4 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 mod- els produce equally good outputs, whereas, for 22% of the inputs, both models generate unacceptable outputs. Table 12 provides a side-by-side compari- son of the outputs generated by the two models.
6.2 Generalizability to Adjaaeet Tasks
6 Qualitative Results
We analyze the generalization capabilities of our models by evaluating them on few related tasks that do not exist in the fine-tuning data. Specifi- cally, we chose two standard NLP tasks sentence compression (SC) (Filippova and Altun, 2013) and politeness transfer (PT) (Madaan and Yang, 2021). is noteworthy that while our models were not finetuned on these exact tasks, we chose them so that the models could still comprehend them based
We now address RQ2 and RQ3 (Section 4). We show that COEDIT shows generalization abilities to adjacent tasks not seen during fine-tuning and can generalize composite instructions containing a combination of tasks. Further, our human eval- uation studies show that expert human evaluators find the text generated by COEDIT to be of higher quality than a much larger instruction- LLM.

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Iee
Fluency
Clarity
Coherence
Style
Model
Size
Immustre
JFLEG!
ASSET!
DiskoPre-Wiki
GYAFC///
WNo///>
MRPC
COEDIT-XL
3B
36.6
42.2
80.5
55.1 /98.3
70.4 48.8
29.3/99.6
COEDIT-XI-
3B
36.5
65.1 /61.
42.0
74,8
69.7 48.5
20.7 /98.8
Table 6: Results for composite prompt training on single-tar task performance. Scores follow exactly Table 2.
COEDIT-XL-C
GPT3-Edit
Tie Neither
tions. Composite instructions are made up of a combination of tasks. For example, for the compos- ite instruction, "Make the text simpler, paraphrase it, and make it formal", the model needs to simulta- neously perform simplification, paraphrasing and formalization of the input sentence.
38%
34%
25%
COEDIT-XL-C COEDIT-XL
Tie
Neither
34%
21%
14%
31%
Table 7: Human evaluation results: Pair-wise compari- of COEDIT-XI--C against GPT3-EDIT and equiv- alent COEDIT-XI (with chaining pipeline). Human annotators preferred the said model for test inputs.
Since there is no publicly available dataset for composite instructions, we create the dataset by expanding the COEDIT dataset to a total of 90k pairs. In addition to the single-task instructions, we use seven new combinations of instructions part of our training set, with each composite instruction having either two or three tasks. Specifically, these are GEC-Paraphrasing, GEC- Simplification, Secc--Praphrrsin-Simpppee Homaalty-Paraphraatt Formality- Simplification, and (more details in SA). We then fine-tune the FLANT5-XL model on COEDIT-COMPOSTA (referred as COEDIT- XI.-C). The training details are summarized in $D.
on other tasks they were fine-tuned on. We de- fine them as being adjacent tasks, which still exist within the scope of existing tasks but have not been seen during tie-ttunig (blue lines in Fig. 2).
Similar to the previous experiment, in addition to GPT3-EDIT, we compare COEDIT-XI against the similarly-sized prefix-tuned (T5-XL) model and the sppeeffe- trained FLANT5 model (same models as the ones used in Table 3 (a) and (b)). For evaluation, we curated a set of new instruc- tional prompts geared towards both the new tasks (details in Appendix C). We evaluated the models on the respective test datasets from Filippova and Altun (2013) and Madaan and Yang (2021).
Table 5 shows the results of COEDIT-XL against various models the sentence compression and politeness transfer tasks. For SC, we report the SARI metric for rewrite quality and compression ratio (CR) for spsk-sp quality. For PT, we re- port Self-BLEU (Zhu et al., 2018) 8) for the rewrite quality² and Transfer Accuracy (TA) for the task- specific quality. We observe that COEDIT con- sistently outperforms other models on both tasks, which indicates its generalization abilities these new and unseen adjacent tasks. It is noteworthy that GPT3-EDIL performs quite well out-of-the- box on PT, but not so much on the SC task.
We evaluate COEDIT-XL-C on both single and composite instructions. For the single instructions, we use the same evaluation setup as in Table 2 and find that the overall performance of CoEDIT- XL-C is on par with that of COEDIT-X xL (Table 6). This shows that training the model additionally composite prompts has negative impact on single-task performance.
For composite instructions, we conduct human evaluations since there no standard test dataset available. We use three new task combinations in addition to the seven seen during training to evaluate the model's generalizability. These are Ccherence-Paraphrasse
Joherence-SSimlifty and Coherence- SimpliffyPPrraphrasss Specifically, we conduct two of pairwise annotations (simi- lar setup as the one in Section 6.1) comparing COEDIT-XI--C with GPT3-EDIT and COEDIT- XL (shown in Table 7) on 30 composite instructions. For fair comparison against COEDIT-XI, we pre-
6.3 Generalizability to Composite Instructions
Finally, also explore the capability of our model understand composite natural language instruc-
"We report Self-BI.R -ulf-BLEUU based on the original PT paper since there are no references provided in the datasct.

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pare a chaining pipeline 10 by decomposing compos- ite instructions into a sequence multiple single in- structions and executing them one-by-one, 38% of cases, experts show a preference for COEDIT- XL-C, compared to 34% for GPT3-EDIT. In 3% cases, both models are preferred equally, whereas, for 25% of the cases, none of them are preferred. The experts prefer COEDIT-XI--C for 34% of the cases versus 21% for the chaining baseline. Both outputs are preferred equally 14% cases, whereas, for 31% of the cases, both models gener- ate unacceptable predictions. Table 13 provides a side-by-side comparison of outputs generated by these models.
work involves prompt sensitivity. While we con- struct our inputs by randomly choosing from pool of verbalizers for every task, we acknowledge that different prompts may induce better or worse ed- its, and as we evaluate each input with a random verbalizer, a fully controlled comparison for cach available prompt across all models is not done. Fur- thermore, the prompting format was kept uniform across all evaluated models, whereas some models may perform better with different prompting for- mat. We plan to address this in future work. Finally, computing resource requirements could pose some difficulty in replicating the results (which we try to address by sharing our models publicly).
7 Conclusions
Ethics Statement
We present COEDIT - an open-sourced dataset and set of instruction-tuned large language models that can act as a writing assistant by following natu- ral language instructions to perform various tex- tual edits by removing, updating, or adding words, phrases, and sentences. COEDIT achieves state-of- the-art performance on multiple text editing bench- marks, spanning syntactic, semantic, and stylistic edit requirements. Through extensive experiments, we have shown that COEDIT is capable of further generalizing to unseen, adjacent, and composite instructions to perform edits along multiple dimen- sions in single turn. In our human evaluations, we observe that COEDIT can assist writers with various aspects of the text revision process at scale by following natural language instructions.
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, perpetuating biases.
A Acknowledgements
We sincerely thank Alice Kaiser-Schatzlein, Robyn Perry, Maya Barzilai, and Claudia Leacock for pro- viding their invaluable linguistic expertise and in- sightful feedback with the evaluations. We also thank Max Gubin, Leonardo Neves, and Vivek Kulkarni for their helpful suggestions.
Limitations
Although COEDIT achieves state-of-the-ar perfor- mance on multiple text editing benchmarks, 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 real-world editing settings remains to be seen. Additionally, our system mainly focuses on non- meaning- 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
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2010. A monolingual tree-based translation model for sentence simplification. In Proceedings of the 23rd International Conference on Computational Lin- guisties (Coling 2010), pages 1353-1361, Beijing, China. Coling 2010 Organizing Committce.
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
we use the following
datasets:
Fluency: We use three prominent corpora for GEC: the NUS Corpus of Learner English (NU- CLE) (Dahlmeier et al., 2013), the W&I-LOCNESS (Bryant et al., 2019), and the NAIST LANG-8 Cor- pus of Learner English (Tajiri et al., 2012), which is one of the largest and most widely used datasets for GEC.
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoe V Le. 2022. Finetuned language mod- els are zero-shot learners. In International Confer- ence on Learning Representations.
Clarity: We split Clarity into two sub-tasks, one focused on Text Simplification, and the other cate- gory focused on the set of edits outside of Simpli- fication. In total, we use five corpora for Clarity tasks: Four of them the NEWSELA corpus (Xu et al., 2015), WIKILARGE (Zhu et al., 2010; Wood- send and Lapata, 2011: Kauchak, 2013), WIKI- AUTO (Jiang et al., 2020) and a subset from PARA- BANKV2 corpus (Hu et al., 2019) focus on text simplification, and the last one comes from the Clarity split of ITERATER (Du et al., 2022b).
Kristian Woodsend and Mirella Lapata. 2011. Learning to simplify sentences with syushhsynchsssuus gram- mar and integer programming. In Proceedings of the 2011 I Conference on Empirical Methods in Natural Language Processing, pages 409-420, Edinburgh, Scotland, UK. Association for Computational Lin- guistics.
Haoran Wu, Wenxuan Wang. Yuxuan Wan, Wenxiang Jiao, and Michael Lyu. 2023. Chatgpt or grammarly? evaluating chatgpt on grammatical error correction benchmark.
Wei Xu, Chris Callison-Buree and Courtney Napoles. 2015. Problems in current text simplification re- search: New data can help. Transactions of the Asso- ciation for Computational Linguistics, 3:283-297.
Coherence: We use the DISCOFUSE dataset (Geva al., 2019), it involves linking two given sentences as coherently as possible using edit oper- ations such as inserting discourse connectives.
Wei Xu, Courtney Napoles, Ellie Pavlick. Quanze Chen, and Chris Callison- Burch. 2016. Optimizing sta- tistical machine translation for text simplification. Transactions of the Association for Computational Linguistics, 4:401-415.
Style: Owing to the subjective nature of STYLE edits based on different sub-intentions (eg. con- veying writers' writing preferences, including emo- tions, tone, and voice, etc.). We use the following datasets for making different stylistic edits to flect those distinctions:
Xingxing Zhang and Mirella Lapata. 2017. Sentence simplification with deep reinforcement learning. In Proceedings of the 2017 Conference Empirical Methods in Natural Language Processing, pages 584- 594, Copenhagen, Denmark. Association for Compu- tational Linguistics.
Formality: We use Grammarly's Yahoo An- swers Formality Corpus (GYAFC) (Rao and Tetreault, 2018) which is a parallel corpus of informal and formal sentence pairs from two different domains.
Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu. 2018. Texygen: A

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Neutralization: We use WNC (Pryzant et al., 2020), dataset from the Subjective Bias Neu- tralization task, where the objective to re- move or mitigate biased words to make sen- tences more neutral;
task-specific verbalizers and concatenate them. The ordering of the single tasks in composite instruc-
tion is also chosen randomly to ensure better gen- eralization.
B Testing Dataset Description
Paraphrasing: For paraphrase generation, used the PARABANK V2 corpus (Hu et al., 2019), since is large-scale corpus that con- tains multiple diverse sentential paraphrases.
Specifically, consider the following datasets:
Grammatical Error Correction We use the JF- LEG (Napoles et al., 2017) corpus of English sen- tences that represents a range of language profi- ciency levels and comprehensive fluency edits. 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.
Once the raw datasets were collected, we ran- domly sampled them to the quantities mentioned in Table 1 based few heuristics such 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 C) to con- vert <source, target> pair to a sinstruction source, target> pair. All our models were then fine-tuned on the verbalized dataset.
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), where each complex sentence consists of multiple crowd- sourced reference simplifications. We report results using the SARI metric.
Table 8 shows the composition of the COEDIT-COMPOSTI dataset, in addition to the details about datasets and prompts. We use seven such composite instructions dur- ing model training. For the first three composite prompts (GEEC-Paraphrasing, GFC-Simplification, we use GEC datasets to extract datapoints that show simpli- fication and paraphrasing edits in addition to GEC. For the next three prompts (Formality- Paraphrasing, saaagaaasinnSSmppiifiattooo we use the formality dataset (GYAFC) to extract pairs which exhibit paraphrasing and simplification edits in addition to formality. Lastly, for the last prompt (Paraphrasing- Simplification), we use the ParabankV V2 paraphras- ing dataset to extract data points which show simplification of the source text in addition to para- phrasing.
Composite instructions:
We use the Coherence split of IT- ER FRATER (Du et al., 2022b), and the DIsCOFUsE dataset (Geva et al., 2019), as it involves linking two given sentences coherently as possible using edit operations such as inserting discourse connec- tives. We report results using the SARI metric.
Coherence
Iterative Text Editing We ITERATER (Du et al., 2022b), iterative text revision dataset span- ning five edit intentions (Section 3) across three different domains (ArXiv, News, Wikipedia). We evaluate our models using the SART metric. We report the performance on individual intentions Fluency, Clarity, and Coherence, and also aggre- gated scores the full dataset, which includes Style edits.
Formality-
To select the appropriate source-target pairs for a composite instruction, we use similar heuristics as with single-task instructions, i.e. old word re- tention, complexity ratios, dependency tree depth ratio, and character length ratio. For example, a source-targed pair from a GEC dataset can be used for the composite instruction involving GEC, para- phrasing and simplification if the target and source sentence has a high edit distance and low complex- ity ratio, character length and word retention scores, The exact details can be found the code. Finally, for building the prompts for the com- posite instructions, we randomly sample from the
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), a parallel corpus of informal and formal sentence pairs from two different do- mains. Similar prior works, we evaluate the quality of rewriting using SARI, and the accuracy of style transfer using a formality classification model"!
"I "https://huggingfaae" wttp://nnaaaaannnnnnnaaee classifier
co/s wtw.snnnppxllrrfoooaliiiiiooee

---

Edit Intention
Datasets
Size
Example Input
Example Output
REC-PARAPHRASE
NUCLE-14
1k
Fix grammar in this tence, and rewrite this sen- tence: How about taking count of psychology?
One such perspective is to take psychology into count.
Lang-8
BEA-19
GEC-SIMPLIFY
NUCLE-14
1k Make this easier to under- stand, and remove grammat- ical mistukey: So it was not like enjoying the tasteful gar- dens.
So was not as if could enjoy the pretty gardens.
Lang-8
BEA-19
GEC PARAPHRASE SIMPLIFY
NUCLE-14 14
1k
Rewrite this sentence, change to simpler wording, and fix the grammar mis- takes: Due to ageing, some of the people may suffer from physical and mental deprociation.
Due to the effects aging, some people may suffer.
Lang-8
BEA-19
FORMALITY- PARPPIRSS
GYAFC
5k
Make this sound more for- mal, and paraphrase: writ- ers dont think about what they will write, they just write!!!
Some writers can write freely without putting too much thought
FORMALITY-O SURMALLYY-SIMPLIPY SIMPLIFY
GYARC
2k Rewrite more formally, and make this text less complex: Not my knowledge.... little curious myself now though.
do think
FORMALITY- FORMALITY-PAAALHRAA---SSS SAMPLIYY
GYARC
4h Rewrite the sentence be simpler, make this sound more formal, and para- phrase this sentence: my is what... clever riddle!!
Your riddle very clever, and I am unsure how spond.
PARAPHRASE- HAAPHRASE-SIMPLIPY SIMPLIFY
ParabankV2 5k
Use simpler wording, and write paruphrused version of the sentence: In your sec- ond communication, re- quested reinforcements.
You asked for backup in your second report
Table Example data instances with composite instructions in the COEDIT-CCMPOST dataset (90K cinstruction: source, target> pairs). Instructional prompts in the inputs are italicized.
Neutralization We use WNC (Pryzant et al., 2020), dataset from the Subjective Bias Neutral- ization task. Based prior works, use Exact- Match (EM) for evaluations, which is the percent- age of examples for which the edited exactly matches the reference(s).
erence texts, and Semantic Similarity" to measure meaning preservation.
C Task Verbalizers
We manually curated a variety of task-specific ver- balizers to construct the instructional inputs. Table shows the full list of the verbalizers used for train- ing and evaluations. Table 10 shows the verbalizers used for the experiments conducted in Section 6.2.
Paraphrasing We 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 Pairs (QQP) datasets. We evalu- ate paraphrasing on two criteria and metrics: Self- BLEU (Zhu et al., 2018) to measure the diversity of the paraphrases relative to the given source and ref-
D Training Details
We used the Adam optimizer with a learning rate of 1e - 4. Each model is trained for 5 epochs
12 "https: httpas:///orraattagogg www.tttta.uurr...ooom com/ Birst-Quora-Datte Reweett--eleeeeeeeeeee Pairss
We use the paraphrase-mpar saapprrasee-mee--bbseeeee bbass-v2 model from Transformrs (Reimers and Gurevych, 2019)

---

Edit Intention
Verbalizers
/ Task
GEC
Fix grammar, Fix grammar in this sentence, Fix grammar in the sentence, Fix grammar errors, Fix grammatical errrrs, 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 grammat- icality in this sentence, Fix grammaticalitty 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 ertors, Remove all grammatical errors from this text, Improve the grammar of this text, Improve the grammaticalityy 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 readahle version the sentence, Write hetter 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 simpler version for the sentence. Rewrite the sentence to be simpler. Rewrite this sentence in 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 simpler wording, Simplify this paragraph. Simplity this text. Use simpler wording. Make this casier understand
Coherenee
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 cnherent, Make the text more cohesive, logically linked and consistent as 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 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 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 view, Make this text less biased
Paraphrasing
Paraphrase the sentence, Paruphrase this sentence, Paraphrase this text, Paraphrase, Write paraphrase for the sentence, Write paraphrased version of the sentence, Rewrite the sentence with different wording, Use diffferent wording. Rewrite this sentence, Reword this sentence, Rephrase this sentence. Rewrite this text, Reword this text, Rephrase this text
Table 9: Complete list of task sseeifpeiiiic verbalizers used our training and test datasets.
Edit Intention
Verbalizers
/ Task
Sentence Com-
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 he shorter, Rewrite this sentence in 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
pression
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 polite manner, Write a more polite manner, Rewrite in more polite manner
Table 10: List of -pecifcc verbalizers used for generalizability experiments.

---

IteraTeR
Clarity
Coherence
Style (Paraphrasing)
Model
Size
ITERAT:R. FLU
Irke.TyR- con
TURK
DiscoFuse-Sprt
STS
00R
COPY TS-LARGE
31.9
28.6
30.8
26.3
30.5
39.6 100
30.1 100
770M
15.1
23.8
34.2
28.4
18.0/
18.2 62.5
TO
38
27.1
28.0
21.2
34.8
33.6
27.7 / 88.7
10.2 63.4
(b) IK-INSTRUCT'
3B
22.3
20.9
21.2
32.3
29.1
12.3/
19.1 79.6
T0++
11B
37.9
30.2
27.5
34.1
35.1
29.9 99.0
16.6/ 79.1
LLAMA
7B
32.4
28.8
31.4
30.8
14/144
1.5/ 35.8
GPT3
175B
20.9
21.8
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
7999
8.17 /96.5
INSTRUCTGPT
175B
43.7
28.1
33,9
38.9
45.8
11.68 88.7
9
GPT3-EDIT
176B
48.3
31.8
34.2
34.7
48.0
14.0/
6.45
ALPACA (FS)
7B
33,0
28.9
31.0
28.4
32.1
0/
0/6
(e) GPT3 (FS)
175B
33,9
30.8
33,9
38.7
46.4
0/2.1
0/
CHATGPT (FS)
36.2
27.0
35.6
37.3
49.4
88.7
0 0/
INSTRUCTGPT
175B
44.7
31.7
37.5
39.5
56.9
0/ /1.88
0 9691
(FS)
ITERATER DELITERATER
570M
36.7
30.4
40.9
24.1 81.4
20.6 94.3
(f)
570M
32.0
28.9
31.7
24.7 87.6
20.4/ 96.6
PEER-3B
3B
51.4
32.1
32.1
32.5
PEER-1IB"
11B
52.1
32.5
32.7
34.1
COEDIT-L
770M
46.8
30.9
31.5
38.5
70.5
22.41 92.8
15.7 197.2
COEDIT-XL
3B
50.4
31.3
31.5
38.5
74.6
20.8 / 94.88
15.4 99.88
COEDIT-XXL
11B
51.6
31.8
31.5
38.2
76.2
21.8 /93.8
15.4/ 98.2
Table 11: Comparison of CoEDIT against various baselines (on sub-tasks and additional datasets to Table divided into seven groups: (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
LLMs, (e) Few-shot evaluations of large pre-trained models 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. (1) indicates higher is better, and (1) indicates lower is better. The perforerfonmng models for each dataset highlighted bolded boxes. (FS) denotes few-shot evaluation.
with early stopping. All models were fine-tuned A100 GPUs using Deepspeed (Rasley al., 2020). Maximum sequence lengths for both the source and the target were set 256 tokens (via filtering). The best persforrnoning checkpoints were chosen based the validation loss.
E Model Performance
Table 11 compares the performance of 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.
F Data Examples

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Instruction
GPT3-EDIT
COEDIT-xI (ours)
Fix all grammattical ermrr: Every one would expect to leave his owns and secrets dreams like to he painter, a writter...
Every one would expect to leave his owns and secrets dreams like the paint- ing 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 British sport believed to derive from the same origins as many raequet sports.
Fives is a British racquet sport.
Fives is British sport. It thought to have come from the same place as many racquet sports.
Fix coherence this Budo incredibly powerful ability. Spike has yet to fully develop this awe- some fighting technique.
Phantom
Phantom Budo is an incredibly power- ful fighting technique. Spike is yet fully develop it.
Phantom Budo is incredibly pow- erful ability, but Spike has yet to fully develop it.
Use different wording: Judge Gerald W. Heaney, in dissent, said the authori- ties should have allowed the prisoner be medicated without the conse- quence of execution.
Judge Gerald W. Heaney, who dis- agreed 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 he treated without exccu- tion.
Table 12: A sample of text revisions generated by GPT3-EDIT and COEDIT-XL (ours).
Instruction
GPT3-EDIT
CoEDIT XL (chaining)
COEDIT-XL-4
Remove all grammatical errors from this text, and make this text less complex: They follow the way Sun goes from east to the west.
The Sun rises the cast and sets the west.
They move with the sun goes from east west.
They follow the goes from east to west.
Make the sentence grammatical, rewrite the sentence with differ- wording, and make this text less complex: Fives is British sport believed to derive from the same origins many racquet sports.
Fives is British sport that is believed to derive from the same origins as many racquet sports.
It is thought to have from the same sources other racquet sports.
Fives is British sport that believed to have origi- nated from racquet sports.
Make the text coherent, and rewrite this text: Nebulous is initially unconcerned when he is unable finish his cheese sandwich. He soon discovers he dealing with an infinite sand- wich and immeasurable garnish.
Nebulous is initially uncon- cemed when he is unable to fin- ish 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 wor- ried about the fact that he couldn't 't finish his cheese sandwich, but soon real- ized that this was an end- less sandwich and an end- less amount of condiments.
Nebulous is initially uncon- cerned when he is unable to finish his cheese sand- wich, but soon realizes he is dealing with an endless sandwich and endless supply of condiments.
Make the text more coherent, simplification, and rewrite this sentence: Phantom Budo is an incredibly powerful abil- ity. Spike has yet to fully develop this awesome fighting technique.
Phantom Budo is an incredibly powerful ability. It is an awe- some fighting technique that Spike has yet to fully develop.
This fighting technique of Spike is still in develop- ment.
Spike has yet to fully de- velop his Phantom Budo ability.
Rewrite the sentence to be sim- pler, and paraphrase this text: The jury asked for transcripts of Quattrone's is testimony about his role in the IPO allocation pro- cess.
The jury asked for transcripts of Frank Quattrone's 's testimony about his role in the initial pub- lic offering allocation process.
The jury requested a copy of Quattrone's 's testimony.
Transcriptions of Quat- trone's 's testimony were re- quested by the jury.
Rewrite more formally, and make this easier to snderstand:: brian. he asked the coolest questions, they suspended him though. dont know what's 's up with that!
Brian, asked the coolest questions, they suspended him though, I don't know what's 's up with that! He was cool guy, and I liked his questions.
do not understand why Brian was suspended. He had some great questions.
I do not know why they sus- pended Brian.
Table 13: A sample of text revisions generated by GPT3-EDIT, COEDIT-XX XL (chaining) and COEDIT-XL -X--C for composite instructions.


Reviews:
[{"review_id": "AJgLSz7eD1", "reviewer": "Reviewer_NJjA", "summary": "", "questions": "1. From the paper, I'm not sure how exactly did the author paraphrase the instruction prompts to provide variations. Is it manually paraphrased or automatically? What's the policy when doing so? How many variations are there for each task?\n2. The observation on Line 440 is interesting, but what exactly do you mean by randomised task-specific instructions? Do you keep the same random instructions for all data in each task? Or they are random within each task? If it is the latter one, I think you need clearer explanation on this observation. \n3. In line 065, you claim \"Our experiments demonstrate that fine-tuning instructions for specific tasks is more effective than multi-task fine-tuning\", I believe by \"multi-task fine-tuning models\" you mean models like ChatGPT and ALPACA. It might be unfair to make such a claim if not having a clear control on the size of the instruction-tuning dataset. Furthermore, in Figure 2, from a reader's perspective, it seems you are suggesting the left general-purpose cannot generalise to composite and unseen tasks.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your time and effort in reviewing our submission. We acknowledge your insightful feedback and comments, and will address your suggestions, and make the following updates in our current submission:\n\n**Single-task Baselines**:\nIn this work, our focus was on multi-task text editing models – where multiple text editing tasks can be performed with a single model. Kim et al. (2022) have demonstrated the benefits of training multi-task text editing models over single-task text editing models in their DELIteraTeR system. While they did not specifically use instruction tuning, other works have observed similar results (e.g., Sanh et al., 2022). Hence, we chose to skip it in the interest of space and the novelty of other results. However, we will address this by adding an additional baseline focusing on single-task text editing models in the final version.\n\n**Generalization**:\nThe reviewer’s observation is accurate, and this was a deliberate choice. We have explained this in lines 499-505:\n\n> “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 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 lines in Fig. 2).”\n\nBy generalization, we mean that the model was able to generalize to “adjacent text editing tasks,” where we have defined adjacent tasks in L502. Studying inter-task dependency is out of the scope of this work, but we agree with the reviewer that the phrasing of “generalizability” here can be misconstrued. We will rename the subsection from “generalizability on out-of-domain tasks” to “generalizability to adjacent text editing tasks” for better clarity.\n\n**Compositional Task Data**\nWe agree with the reviewer's observations about how we built the training dataset and that it does not reflect all possible cases of compositionality (since we chose only 7/120 possible combinations). This was a deliberate choice as there does not exist any compositional dataset for text editing, and collecting human-annotated datasets is expensive. Therefore, we leveraged the potential overlaps between tasks and used task-specific metrics to create compositional and multi-task datasets from single-task datasets. Thus, our aim with this experiment was to study compositionality within text editing and show that even with this simple setup, it is possible for much smaller models to outperform larger general-purpose LLMs. In the future, we will look into studying broader compositionality setups with higher-quality data. \n\nAs to **how we created the compositionality data**, we used three new task combinations (in addition to the seven used during training) to evaluate the compositional generalizability in our experiments. These are Coherence-Paraphrase, Coherence-Simplify, and Coherence-Simplify-Paraphrase. Due to space constraints, we mention this information in the appendix (L1150). We will include this in the main body of the paper in the revised version. \n\nRegarding **using 30 composite instructions** in Table 7 for evaluation, we acknowledge this feedback. Our decision was based on the fact that there is a lack of an existing standard evaluation dataset to measure compositional generalizability and the challenges related to the time and costs associated with human evaluation. \n\n***Responses to questions***: \n\n**Instruction Prompt Creation**: We have provided the full set of instruction prompts in Table 9. These were created heuristically by using an initial list of instructions that were paraphrased using rules to ensure lexical and semantic diversity. The final list was then chosen by manual filtering of the candidates. We will release these scripts in our code for full reproducibility. \n\nBy **randomized Task-Specific Instructions**, we mean that we combine all instructions (from all tasks) into a list and then randomly sample an instruction to be prepended to a source text. In this way, it is possible that a simplification data point might get an instruction for grammatical error correction, and vice-versa, and so on. Hence, the instructions are randomized across the entire dataset.\n\n**Line 065**: We acknowledge the feedback on this point. With our current phrasing, we are not saying that “left general-purpose cannot generalize to composite and unseen tasks”; we are instead saying that task-specific (right) can generalize better to composite and unseen tasks since they are closer in the “task space.” We will ensure we edit the paper's text to convey the right meaning. \nWe will also fix the other issues pointed out by the reviewer pertaining to naming the “Baseline” to “Copy Baseline” and adding PEER’s base model.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper has shown instruction-tuning can effectively push a range of text-editing tasks into one LM with good performance. Extensive experiments are done to support the conclusion.  \n2. The paper mentioned an interesting and important topic, generalisability of the instruction tuning. However, the experiments setting is a bit weak. ", "reasons_to_reject": "\n1. Key baseline is missing in the performance experiment:\n    The author has compared with a series of baseline models in Table 2. However, it could be important to compare with single text-editing task fine-tuned Flan-T5. This could illustrate if the multi-text-editing-task instruction tuning can effectively improve the performance.\n\n2. The argument on the performance on generalisation is a bit weak:  \n    - For the experiments on generalisability to unseen tasks, the task choices are sentence compression and politeness transfer. I have concern that the sentence compression is very similar to the text simplification task, which is used as training data. The politeness transfer task also has similarity with the formalising task. If you think there are clear differences, please explain the similarity and difference a bit more, or if possible choose a different task as unseen task.\n    - The way you collect the training data for compositional task, mentioned in line 1130, is selecting special cases from the current GEC and GYAFC training data. If they are compositional enough are questionable. \n    - In line 558, you didn't tell how many new task combinations did you use to evaluate the compositional generalisability. Furthermore, the test results in Table 7 are only from 30 composite instructions. ", "questions_to_authors": "1. From the paper, I'm not sure how exactly did the author paraphrase the instruction prompts to provide variations. Is it manually paraphrased or automatically? What's the policy when doing so? How many variations are there for each task?\n2. The observation on Line 440 is interesting, but what exactly do you mean by randomised task-specific instructions? Do you keep the same random instructions for all data in each task? Or they are random within each task? If it is the latter one, I think you need clearer explanation on this observation. \n3. In line 065, you claim \"Our experiments demonstrate that fine-tuning instructions for specific tasks is more effective than multi-task fine-tuning\", I believe by \"multi-task fine-tuning models\" you mean models like ChatGPT and ALPACA. It might be unfair to make such a claim if not having a clear control on the size of the instruction-tuning dataset. Furthermore, in Figure 2, from a reader's perspective, it seems you are suggesting the left general-purpose cannot generalise to composite and unseen tasks.", "paper_topic_and_main_contributions": "In this paper, the author fine-tuned a LM, Flan-T5 (L, XL, XXL) with a set of text editing dataset and instructions.  \nThe author explored two main research aspect 1) How good can such a text-editing instruction fine-tuned LM perform. 2) Can such a model be generalised to unseen text-editing task or compositional text-editing task?  \n\nAccording to the author, the **contributions** of this work are:\n1. It fine-tuned a LM with various text-editing tasks and achieved SOTA performance on most of them.\n2. The purposed fine-tuned LM is parameter-efficient compared with other text-editing models.\n3. The purposed model has good generalisation performance on both unseen and compositional text-editing tasks.\n4. The fine-tuned model and the data used will be released.\n(However, I have concerns towards point 2 and 3. Please see the Reasons to Reject below.)\n\nThe training data are a set of public text editing dataset, covering tasks of fluency, coherence, simplification, paraphrasing, formalising and neutralising. The author mainly focus on non-meaning-changing editing tasks. The author rewrites the instruction prompts to provide variation.  \n\nThe author conducts **3 main experiments**:\n- Text editing performance on the test set of the fine-tuning dataset. The purposed model is compared with a series of baseline models, including zero-shoting or few-shoting. Human evaluation on comparing the purposed model with GPT3-Edit model.\n- Ablation studies to investigate the effects of 1) general instruction tuning, 2) text-editing tasks training 3) instruction quality.\n- Generalisation performance on unseen and compositional text editing tasks. \n\n", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "1. In section 4.1 the heading of the first paragraph is **Baseline**, but the latter paragraphs are also baselines.\n2. The formulation of Table 2 can potentially be improved: Should show clear labels on which baselines are general instruction-tuned, zero-shot or few-shot. The ordering of the baseline could make better sense if following increasing model size, instead of the current group arrangement.\n3. In line 257, it might be a good idea to mention what's the base model of PEER."}}, {"review_id": "nLG4PQzag5", "reviewer": "Reviewer_MrqV", "summary": "", "questions": "1. What's the detail about the few-shot learning? E.g., how many samples are selected; necessary token length change after few-shot setting, etc.\n2. I feel confusing about the term 'prompt-tuned' large pre-trained models. What do you mean by this?\n", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate your time and effort in reviewing our submission. We acknowledge your feedback and comments. The main feedback is in terms of novelty, and the following is our response:\n\nIn terms of novelty, we believe our work is novel/valuable to the community in two main ways:\n\n**Empirical novelty**\n- Our work is the first to gather task-specific datasets, leverage instruction-tuning, and show that a single model can achieve state-of-the-art results on multiple text editing benchmarks. \n- The related works (covered in Section 2) 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.\n\n\n**Practical novelty**\n- Our models are over 60x smaller than the other publicly available ones we compare against, and achieve a better performance. This is impactful for numerous reasons – it will make writing assistants easier to deploy, and cut down serving costs, which are prohibitively expensive. \n- We release our code, data, and models publicly – this is a crucial contribution to the community, as there do not currently exist any replicable, open-source models focused on general-purpose text editing that can perform text editing with high quality while being small-sized (3-13B parameters). The closest one to our work (PEER) is neither as performant, nor publicly available. \n\n**Response to questions**\n\nWe conducted the few-shot experiments under a 4-shot setting to account for the maximum input length of the smallest model tested. We randomly selected four examples per intent from the CoEdIT training dataset, which were prepended to the instructional prompt as part of the input in the format: \n```\n<prefix>: <source> \nAnswer: <target>\n```\nwhere we took specific inspiration from the few-shot experiments in Brown et al. (2020) that prepended examples to the input in the format:\n\n```\nPoor English input: I eated the purple berries. \nGood English output: I ate the purple berries. \nPoor English input: Thank you for picking me as your designer. I’d appreciate it.\nGood English output: <completion>\n```\n\nAs for the term ‘prompt-tuned,’ we utilized this term and ‘few-shot’ interchangeably for describing and showing examples of good revisions as part of the instructional input. \n\nWe will replace all occurrences of “prompt-tuned” with “few-shot” for uniformity across the paper and clarity of terminology. We will also make the details of these experiments clearer.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1.The proposed method is able to achieve better results with less parameter on text-editing task.\n2. The comparison between the proposed method and existing methods is extensive and showing strong support to the claimed contribution in the paper.\n3. There is some insight discussion about the general-purpose and task-specific tuning regarding to text-editing task.\n4. The implementation details including the prompts are clearly described in the paper, making it easier for others to reproduce.\n", "reasons_to_reject": "1. The novelty is limited due to the scope of the idea. While fine-tuning LLM can be applied to different tasks, the proposed method aligns with the typical approach within this paradigm. Consequently, the novelty is restricted by the commonly referred method and the limitations of the particular task.\n\n", "questions_to_authors": "1. What's the detail about the few-shot learning? E.g., how many samples are selected; necessary token length change after few-shot setting, etc.\n2. I feel confusing about the term 'prompt-tuned' large pre-trained models. What do you mean by this?\n", "paper_topic_and_main_contributions": "This work proposes to use fine-tuned large language model to approach text editing task.\nIt conducts extensive evaluations comparing with baselines including existing text-editing models, instruction-tuned LLMs and general-purpose LLMs, showing edited results generated by the proposed method have better quality.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Justification_for_Ethical_Concerns": "n/a"}, "extra_text": {}}, {"review_id": "4CrOHROdx2", "reviewer": "Reviewer_UcMe", "summary": "", "questions": "LN299, \"... the input sentences and its corresponding revised reference were pre-pended to the instructional prompt\". Simply adding these texts may not sound optimal as instruction prompts, and redundant for well-understood task like text-editing. Have you tried to only give the same triplets as you did in CoEdit dataset to these models? e.g., <please simply the text, source, target>", "limitations": "", "rating": 3, "confidence": 3, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate your time and effort in reviewing our submission. We acknowledge your feedback and comments. The following is our response: \n\n**Details of model training and source of performance variations**\n\nWe have shared the details of training datasets, evaluation datasets, evaluation metrics, task verbalizers, and training configurations in Appendixes A-D. Appendix E has details on example data points (showing the input and outputs of different models).\nIn terms of the sources of improvements, we show it via our ablation studies. Specifically, there are two main sources of improvement:\n- Instruction-tuning (vs. prefix-tuning) and the underlying FlanT5 models \n- Task-specific training (via the high-quality human-annotated datasets and the dense task space)\n\n**Novelty/Differences against PEER**\n\nWhile we have highlighted the overlap with PEER in the related work, we have also described how there is a significant difference in the two works (L131-144).\n\nOur work is focused on text editing and revision (different problem scopes), uses task-specific datasets and instruction-tuning models (different modeling approaches), and further investigates different parts of the problem space (adjacent tasks and compositional instructions). \n\nMoreover, we publicly release our code, data, and models – this is a crucial contribution to the community, as there do not currently exist any replicable, open-source models focused on general-purpose text editing that can perform text editing with high quality while being small-sized (3-13B parameters). PEER is neither as performant, nor publicly available. \n\n\n**Dataset Creation (L77)**\n\nWe agree that the dataset creation in this work is not a significant amount of effort, as it is sourced from publicly available datasets, similar to many prior works. However, we are not claiming the dataset as a novel contribution, other than the fact that we release our datasets publicly. The focus of the paper was to improve text editing performance with task-specific instruction-tuned models. \n\n**Single-instruction prompting (L299)**\n\nThe instructional prompts were constructed in a similar matter to the triplets within the CoEdit dataset and particularly taken inspiration from the few-shot setup in Brown et al. (2020):\n```\nPoor English input: I eated the purple berries. \nGood English output: I ate the purple berries. \nPoor English input: Thank you for picking me as your designer. I’d appreciate it.\nGood English output: Thank you for choosing me as your designer. I appreciate it. \n```\nthat was utilized for correcting grammatical errors. We constructed our in-context examples in the format:\n```\n<please simplify the text>: <source> \nAnswer: <target>\n```\nin which we gave four examples per intent that were prepended to the instructional prompt for each instance. We will include this description of the setup in the updated revision of the paper to clarify any confusion about the experimental setup under the few-shot setting.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Rather extensive benchmark comparison is included in this paper with all major editing systems. The introduced model achieves competitive results with them while maintaining much smaller model size. The author conducted experiments on composite text editing by human evaluation, which has been less discussed in previous works.\n", "reasons_to_reject": "This major change in the proposed work is on fine-tuning text editing models using natural instruction instead of plain prefix, and thus using the newly constructed CoEdit dataset. I may have missed it but Section 3 didn’t include further details about the model training based on previous work of FlanT5. It is unclear then what contributes most to the performance variations, and especially in comparing with general purpose LLMs on one specialized task. The more comparable work of PEER (Schick et al., 2023) also tackles slightly wider range of problems as the author also pointed out in LN139. Since instruction based tuning has been explored in many works, the novelty at this point can be limited.\n\nIn the meantime, the effort implied in the dataset construction is less significant, as it is constructed from public ITERATER+  and as said in LN077 “based on rules that introduce lexical and semantic variations”.  ", "questions_to_authors": "LN299, \"... the input sentences and its corresponding revised reference were pre-pended to the instructional prompt\". Simply adding these texts may not sound optimal as instruction prompts, and redundant for well-understood task like text-editing. Have you tried to only give the same triplets as you did in CoEdit dataset to these models? e.g., <please simply the text, source, target>", "paper_topic_and_main_contributions": "This paper describes a text editing system based on more human-readable task instructions such as “make the sentence simpler” instead of fixed actions. The authors constructed a dataset CoEdit from public text editing dataset ITERATER by converting the editing intentions into human instruction triplets <instruction: source, target>. The dataset is then used to fine-tune varieties of FlanT5 models. These models are compared against a list of major editing systems and LLM models with both quantitative and qualitative evaluations on several other editing benchmarks. A human evaluation is also included to examine the performance of performing composite editing involving more than one editing task.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "The CoEdit has been reused in different places to refer to both the model and dataset. This can be confusing in the reading and it may be better to have more clear indications."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper introduces LLMs that are able to perform multiple text editing tasks, while beating strong baselines, using fewer parameters and being faster than existing models. The training approach is based on fine-tuing Flan-T5 models of different sizes using a large set of instructions.

Reviewers praised the extensiveness of the experiments presented, comparing their models with several benchmarks, which allow to trust the conclusions related to performance. Other strengths of the paper include an analysis of the performance of the models in adjancent tasks and in composite tasks, including human assessments on output samples.

The main concern raised by reviewers is related to the novelty of the proposed approach compared to existing work (e.g. PEER), and the fact that fine-tuning LLMs is a common technique in the area. The authors' rebuttal has clarified where the novelty of their work lays, which includes practical reasons (e.g. inference speed, model size, etc.). Authors are also encouraged to include clarifications regarding "generalisability" since "adjacent tasks" would be more suitable than "out-of-domain", as they have also acknowledged.
Decision: Accept-Findings