ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness
Authors: Jan Cegin
Jakub Simko
Peter Brusilovsky
Venue: EMNLP 2023 Main (2023)
Keywords: natural language generation
paraphrase generation
crowdsourcing
large language models
intent classification
text diversity
TL;DR: ChatGPT can produce diverse paraphrases that lack named entity variations, while models trained on such data are robust on OOD data.

Abstract: The emergence of generative large language models (LLMs) raises the question: what will be its impact on crowdsourcing? Traditionally, crowdsourcing has been used for acquiring solutions to a wide variety of human-intelligence tasks, including ones involving text generation, modification or evaluation. For some of these tasks, models like ChatGPT can potentially substitute human workers. In this study, we investigate whether this is the case for the task of paraphrase generation for intent classification. We apply data collection methodology of an existing crowdsourcing study (similar scale, prompts and seed data) using ChatGPT and Falcon-40B. We show that ChatGPT-created paraphrases are more diverse and lead to at least as robust models.

ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness
Jan Cegin Jakub Simko", Peter Brusilovsky
Faculty of Information Technology, Brno University of Technology, Brno, Czechia Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia jakub. simko) @ kinit.sk University of Pittsburgh, Pitssurgh,, USA peterbêpitt, edu
Abstract
The advent of generative large language mod- els (LLMs) and ChatGPT in particular opens up the possibility of replacement of crowd workers by AI. LLMs have been already investigated for a variety of NLP tasks: translation (Jiao et al., 2023), question answering, sentiment analysis, or sum- marization (Qin et al., 2023), displaying strong performance against tine-tuned models. Indeed, some crowd workers have already started to exploit LLMs replace themselves (Veselovsky al., 2023).
The emergence of generative large language models (LL.M)) raises the question: what will be its impact on crowdsourcing? Traditionally, crowdsoureing has been used for acquiring so- lutions to wide variety of human-ine intelnnntelligeenee tasks, including ones involving text generation, modification or evaluation, tasks, models like ChatGPT can potentially sub- stitute human workers. In this study, we in- vestigate whether this is the for the task of paraphrase generation for intent classifica- tion. We apply data collection methodology of existing crowdsourcing study (similar scale, prompts and seed data) using ChatGPT and Falcon-40B. We show that CharGPT-created paraphrases are more diverse and lead to at least as robust models.
some of these
With this paper, we investigate whether Chat- GPT can replace crowd workers in paraphrase generation for intent classification dataset creation. We answer the following research questions:
RQI: Can ChatGPT generate valid solutions to paraphrasing task, given similar instruc- tions as crowd workers?
1 Introduction
Crowdsourcing has been established practice for collecting training validation examples for NLP tasks, including the intent classification (i.e. deter- mining the purpose behind a phrase or sentence). When crowdsourcing intent examples, workers typ- ically create new phrases for some scenario (Wang et al., 2012; Larson et al., 2020). However, data augmentation approach can also be used: by work- ers paraphrasing existing sentences with known intents. The aim is to increase the data diversity and subsequent model robustness. In particular, diversity can increase considerably with the use of taboo words that force workers to more cre- ative (Larson et al., 2020). Without data diversity, models can lose their ability to generalize well (Lar- son et al., 2020; Joshi and He, 2022; Wang et al., 2022).
RQ2: How do ChatGPT and crowd solutions compare in terms of their lexical and syntacti- cal diversity?
RO3: How do ChatGPT and crowd solutions compare in terms of robustness of intent clas- sification models trained on such data?
To answer these questions, have conducted quasi-replication of an existing study (Larson al., 2020), where paraphrases were crowdsourced augment intent classification data (using also taboo words technique to induce more example diversity). In our own study, we followed the same protocol (seeds, taboo words, scale), but replaced the crowd workers with ChatGPT (for approximately 1:600 lesser price) as the most widely used LLM and Falcon-40B (Almazrouei et al., 2023) one of the best performing open source LLM at the time of writing this paper. This allowed us to directly compare properties of crowd- and LLM--genrrated data, with following findings: 1) ChatGPT is highly
Unfortunately, crowdsourcing has several down- sides: 1) the workforce is costly, 2) output quality is difficult to achieve (which translates to further costs), and 3) there are overheads related to the design and organization of the process.

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reliable in generating valid paraphrases, 2) Falcon- 40B struggled in generating valid and unique para- phrases, 3) ChatGPT outputs lexically and syntac- tically more diverse data than human workers, and 4) models trained on ChatGPT data have compara- ble robustness to those trained on crowd-generate data.
produce good results.
In number of recent studies, ChatGPT has been applied to a variety of tasks. has been used as a mathematical solver where it achieves below average human performance (Frieder et al.. 2023), sentiment analysis task with varying per- formance (Zhu et al., 2023) and also as general NLP task solver where it shows good performance reasoning tasks (Qin et al., 2023). Another study (Wang et al., 2023) measured ChatGPTs capabilites to evaluate outputs of several natural language generation tasks, achieving performance of human evaluators. ChatGPT performs well in translation of high resource languages (Jiao et al.. 2023). As for some other typical crowdsourcing tasks, ChatGPT outperforms the crowd in classifi- cation of political affiliation of Twitter users (Töm- berg, 2023) and in-text annotation tasks (Gilardi et al., 2023). When (zero shot) ChatGPT com- pared with a finetuned BERT, ChatGPT falls short in detection of paraphrases and text similarity tasks, but performs well on sentiment analysis, QA and inference (Zhong et al., 2023). These results indi- cate good capabilities of ChatGPT for NLP tasks, and its potential to replace crowd workers in at least some tasks (Törnberg, 2023; Gilardi et al., 2023).
2 Related work: Collecting paraphrases and using ChatGPT
Crowdsourcing of paraphrases is used for creat- ing new datasets for various NLP tasks (Larson et al., 2019a, 2020). In paraphrase crowdsoureing, the process typically entails showing of an initial seed sentence to the worker, who is then asked to paraphrase the seed new variations (Ravichander et al., 2017). As training sample diversity is corre- lated with robustness downstream models (Lar- son et al., 2020; Joshi and He, 2022; Wang et al., 2022), various diversity incentives are used to en- courage crowd workers to create more diverse para- phrases. In some approaches, workers are hinted with word suggestions (Yaghoub-Zadeh-HFaa et al.. 2019; Rhys Cox et al., 2021). Syntactic structures can also be suggested (Ramírez et al., 2022). An- other approach is chaining, where paraphrases from previous workers are used as seeds (Rhys Cox et al., 2021). Yet another technique is the use of taboo words, where users are explicitly told to avoid cer- tain phrases. Previous research has shown, that taboo words lead to more diverse paraphrases and more robust models (Larson et al., 2020; Ramírez al., 2022). Yet, despite all the advances, crowd- sourcing remains expensive for dataset building. Seq2seq models LLMs have already found their use for paraphrase generation from existing cor- pora (GPT-2 (Radford et al., 2019), BART (Lewis et al., 2020)). LLMs also be used to para- phrase using style transfer (Krishna et al., 2020) (resulting paraphrases adhere certain language style). To increase paraphrase diversity, syntax con- trol approaches can be used (Goyal and Durrett, 2020; Chen et al., 2020). Promising results have been achieved in zero-shot settings to produce para- phrases in multi-lingual domains (Thompson and Post, 2020), finetuning using only prompt tun- ing and Low Rank Adaptation (Chowdhury al., 2022). These previous works showed the capabil- ities of L.Mss to produce diverse and valid para- phrases, albeit with various syntax or semantic re- strictions and additional mechanisms needed to
ChatGPT paraphrase validity and diversity
To test whether ChatGPT can be an alternative crowd workers in paraphrase generation, we repli- cated the data collection process (Larson et al., 2020), who crowdsourced paraphrases to create datasets for the intent classification task. In their study, workers created paraphrases of seed sen- tences (with known intents). We replicate their process using ChatGPT and show that ChatGPT generated data are more diverse both in terms of lexical and syntactical diversity than those created by humans.
3.1 Data collection using ChatGPT
Larson et al. (2020) crowdsourced paraphrases for 10 intent classes (centered on the personal finances domain), using 3 sentences per class as seeds. For each given seed, worker was asked to provide 5 paraphrases. Interested in increasing the resulting data diversity (and the downstream intent classifi- cation model robustness), Larson et al. collected data in two modes (to compare their outcomes):

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1st round data collection
2nd round data collection
3rd round data collection
Resulting dataset
Prompt data
Prompt 2 data
Prompt data
Prompt data
Taboo data
Taboo 2 data
Taboo data
Figure 1: The paraphrases created in three rounds, using two modes of worker prompting, Five datasets created were combined into two final datasets (prompt and taboo) used for further comparisons.
1. Prompt, where only seed sentence was shown to worker (along with the instructions to paraphrase it).
Data collection carried out on 5 May 2023, using the gpt-3.5-turbo-000/ model checkpoint. Our instructions to ChatGPT followed the general outlook of prompts given workers in the original study. The ChatGPT prompt itself had this struc- ture: "Rephrase an original question or statement times. Original phrase: [seed phrase]". Along with the prompt, ChatGPT also accepts a "system" message which we specified follows: "You are a crowdsourcing worker that earns living through creating paraphrases." This was to enable Chat- GPT better play the paraphraser role. For the Taboo mode the same prompt was used, amended with the following sentence: "Don't use the words [taboo_]], [taboo_2] ... or [taboo_n] in your responses.", where taboo_n _n represents the taboo words for the given seed. As in the original study, used 3 taboo words for the second round and 6 taboo words for the third round.
2. Taboo, where prompt was shown along with list of taboo words that workers should avoid when paraphrasing. The taboo words were se- lected from words overrepresented in previous paraphrases.
Larson et al. collected data in three rounds, illus- trated in Figure 1. In the first round, workers cre- ated paraphrases using Prompr only method (since no taboo words could be known at that stage). The second and third rounds were conducted in Prompt mode (same instructions the first round) and also in Taboo mode, where taboo words were de- termined based on previous rounds. The five result- ing datasets were then combined into two: prompt dataset (which included all paraphrases gathered via Prompt mode) and taboo dataset (which in- cluded Prompt mode data from the first round and Taboo mode data from the second and third rounds).
To execute requests to ChatGPT, we used the chat completion API 3, The entire example request can be in Appendix A. As for the other param- eters, set temperature
(the higher the value the more randomness in the output), n to 13 (num- ber of returned responses). model to gpt-3.5-turbo and the presence penalty to 1.5 (positive values pe- nalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line). Other parameters were kept to their default values. The parameters mained the same for all iterations and variations of the data collection.
In our own study, we replaced the crowd work- ers with ChatGPT and retained as much as possible from the original protocol of Larson et al. Specif- ically, we kept the three rounds and two modes of data collection and also created two resulting datasets (Prompt GPT and Taboo GPT). We used the same intent classes and seed sentences. We collected similar number of samples (see Table 1). As slight protocol alteration, we chose the same taboo words as Larson al. (instead of computing our own based on the first, resp. sec- ond collection rounds). We did this to allow better comparison between Taboo human and Taboo GPT datasets
In round of data collection, collected paraphrases for each of the 10 intents, with seed phrases each. For each seed, we gathered 13 unique responses, expecting different paraphrases in
hhttps://api..pem.. aegmmaiiccom////chtt///ooaleaaaa

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each. For one data collection round, collected 1950 paraphrases from ChatGPT. This results into 5850 paraphrases for both the Prompt and Taboo dataset (including duplicates and non-valid para- phrased, which are detailed in the next section).
style (usually using very formal language; see ex- amples in Table 2). We also observed that ChatGPT refrains from using slang or non-valid grammati- cal forms. We speculate that this may be due to the "role" given to the model through its system message (see section 3.1) or due to the extended vocabulary often found in formal documents.
3.2 ChatGPT data characteristics and validity
For the data acquired through Taboo mode we also checked if ChatGPT adheres to the taboo in- struction. In the original study, the solutions that did contain the tabooed words were not included in the resulting data (we thus can't evaluate the crowd performance in this regard). In ChatGPT Taboo data, we can see CharGPT breaking the taboo word rule in a minority of cases. For the first round of taboo data collection (2nd round overall), where taboo words were given to ChatGPT per task, we found out that ChatGPT ignored the taboo instruction for 167 out of 1950 samples. In the second round of taboo data collection (3rd overall), where 6 taboo words were given to ChatGPT per task, ChatGPT ignored these instructions for 331 samples out of 1950. Following the original study protocol, we removed samples containing taboo words. This resulted in the final 5143 samples for the Taboo dataset collected via ChatGPT.
We analyzed the ChatGPT-gennraan paraphrases and compared them against the crowdsourced data from the original study. To answer RQI, we as- sessed the paraphrase validity.
First, we counted and filtered the duplicated solu- tions. The ChatGPT generated data contain more duplicates than crowdsourced data (by exact string matching). ChatGPT also generated small num- ber of blank strings (blank lines). From the original 5850 samples of the ChatGPT Prompt dataset have found that 20 blank texts and 610 are dupli- cates. After cleaning, this resulted in 5170 unique paraphrases from ChatGPT collected using the Prompt mode. In comparison, the crowdsourced Prompt dataset from the original study had 6091 collected samples out of which 442 were dupli- cates, totaling 5649 samples. The ChatGPT Taboo dataset contained 10 blank paraphrases and 196 du- plicates, totaling 5608 valid samples. The crowd- sourced Taboo dataset had 5999 samples with 58 duplicates, resulting in 5941 samples in total.
ChatGPT-generated samples are on average longer than those collected from humans. Visual- ization of the distributions of the number of unique words and lengths of generated samples can be found in Appendix in the Figure 3.
Next, we have manually evaluated the validity of all ChatGPT paraphrases, i.e. we checked whether they are semantically equivalent the seed sen- tences and their intents. To validate the created paraphrases we used a simple web application that we developed for this task. The user, who was one of the authors, was shown the seed samples, from which ChatGPT generated the paraphrases, and one particular paraphrase to validate. The author was then able either mark the paraphrase as valid or not, with an additional optional checkbox to label the paraphrase as 'borderline case" for possible re- visions. As the seed sentence changed only once in a while (one time for each 5-20 paraphrases) this significantly reduced the cognitive load on the annotator. We also discussed the 'borderline cases' where the user was not sure about the validity of created paraphrases. All paraphrases were valid and intent-adhering, we therefore conclude the RQ1 with positive answer with some limitations. We noticed some stylistic divergences in the created paraphrases. Approximately 5% of the times, the paraphrases created by ChatGPT are odd in their
3.3 Diversity of ChatGPT paraphrases
To answer RQ2, we measured the lexical and syn- tactical diversity of the collected datasets. Our findings are summarized in Table 1.
We evaluated the lexical diversity in the same way in the original study: by vocabulary size of different datasets. From this point of view, the smallest vocabulary (number of unique words) can be observed in the crowdsourced prompt mode data with 946 unique words. Compared to this, the ChatGPT prompt mode data features 1218 unique words, which is 28.75% increase in vocabulary size. The crowdsourced dataset collected through the taboo mode had even higher vocabulary size with 1487 unique words. However, the ChatGPT taboo mode beats it even more with 1656 unique words (an increase of 1 11.37%). We conclude that data collected via ChatGPT has higher lexical di- versity compared to crowdsourced data when the

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Dataset type
# collected samples
# after filtering
# unique words (1)
TED (1)
Prompt human
6091
5649
946
13,686
Taboo human
5999
5941
1487
15,483
Prompt Falcon
5850
2897
810
14.382
Taboo Falcon
5850
1646
643
25.852
Prompt GPT
5850
5170
1218
19,001
Taboo GPT
5850
5143
1656
18. .422
Taboo GPT+tab. samples
5850
5608
1871
18.661
The datasets used for comparisons in this work. The data originally crowdsourced by Larson are denoted "human", while data collected in our work denoted GPT and Falcon. The GPT data have higher lexical and syntactical diversity than human data (within collection modes) and contain slightly more duplicates, while the Falcon data contained lot of invalid samples. Using taboo mode increases the no. unique words for GPT data with the up arrow indicating "the higher the better".
Table 1:
same data collection mode is used. This can also be seen on the visualization the number of unique words in Figure 3.
3.4
Comparison of ChatGPT paraphrases with Falcon
As ChatGPT a closed model, we sought com- pare its capability to produce paraphrases with an open source LLM. For this, we selected Falcon- 40B-instruct 4 as one of the leading open source LLMs at the time of writing of this paper. We did not conduct any specific parameter tuning, used the same parameter values for ChatGPT, collected the same amount of data as with ChatGPT and used the same prompts as we did for the ChatGPT ex- periments (with only minimal alterations required by the model). Our findings are summarized Table 1.
We also compare the collected datasets on syn- tactical diversity to better assess the structural vari- ations in paraphrases. We do this by calculating the tree edit distance value (TED) (Chen et al., 2019) between all pairs of paraphrases sharing the same intent. TED is first calculated pair-wise for each phrase in the intent and data split (separate for human, GPT, original). This is then averaged get the mean this should represent how syntac- tically diverse the phrases are the higher the num- ber of mean TED is, the more diversity present. When comparing prompt datasets, ChatGPT cre- ated more diverse sentence structures with a mean TED value of 19.001 compared to 13.686 mean TED value for crowdsourced data. The same holds for the taboo datasets: the crowdsourced taboo dataset has a mean TED value of 15.483 while the ChatGPT collected dataset has 18.442. should be noted that while the data collected using hu- man workers have higher TED value for the taboo method than for the prompt method, the same can- not be said about the data generated from ChatGPT the introduction of taboo words to ChatGPT does not increase the syntactical diversity. We have con- firmed findings by running Mann-Whitney-U test between datasets with p = 0.001 for the mea- sured TED values. We conclude that data collected via ChatGPT has higher syntactical diversity than that collected from human workers for the same data collection method.
We preprocessed the data removed duplicates and for Taboo split removed paraphrases which contained tabooed words. This resulted in 3784 samples for the Prompt split and 2253 samples for the Taboo split. When compared with results of ChatGPT collected data in Table 1, the Falcon collected data contain more duplicates and invalid sentences, which resulted in fewer samples.
Next, we manually annotated the collected data with the same process as explained above. Com- pared to ChatGPT, where we detected no para- phrases that had altered meaning, the Falcon col- lected data was of considerably lower quality. The Falcon model struggled to produce paraphrases and follow the basic instructions at times: the created paraphrases were often meta-comments (e.g. 'As an Al language model..' missed the intent of the seed paraphrase or were responses to the seed para- phrases (e.g. from seed 'When is the bank open' the response was 'Which bank do you mean?").
Turning back RQ2, we therefore conclude that ChatGPT generates more diverse paraphrases than crowd workers.

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Intent
ChatGPT generated phrases
Odd ChatGPT generated phrases
phone
How can I connect with my bank over the phone?
Is it possible to share the telephone directory of my financial establishment with me?
location
Can you guide to the nearby loca- tion bank?
Which area has institution mainly dedicated to handling money matters?
balance
you tell me my financial status?
What the procedure review my remaining funds?
safe
Could you help me find a way to keep my valuable items at the bank?
Are there any options available for me to keep my precious belongings in protected setting at the banking establishment?
hours
What is the hour when the bank starts operating?
At what moment do the bank employees arrive to commence their workday?
Table 2: Examples of collected phrases for the Taboo method via ChatGPT. We list some typical phrases created by ChatGPT, as well as some "odd" phrases.
This resulted in future filtering of the datasets per split. For the Prompt split we removed 887 (23.44%) samples resulting in 2897 valid samples and for the Taboo split we removed 607 (26.94%) samples resulting in 1646 valid samples. Finally, we computed the no. unique words in the splits with 810 unique words in the Prompt split and 634 unique words in the Taboo split and the TED value for the Prompt split was 14.382 and 25.852 for the Taboo split.
4.1 Data and models used in the experiment
The data we used for diversity evaluation (section 3) did not include suitable OOD test data for robust- ness tests. Therefore, we turned to a set of ex- isting benchmark datasets for intent classification. As part of their work, Larson et al. sampled these 5 datasets for seeds to create paraphrases through crowdsoureing. As previously, replicated the data collection using ChatGPT instead. The seeds used in this data collection constitute only frac- tion of the original 5 datasets: therefore, we could take the remainder to act as our OOD test data.
The higher TED values (or higher syntactical diversity) and lower lexical diversity for Taboo split when compared to the Prompt split Falcon- collected data can be interpreted be due to the lower amount of valid samples after filtering in the Taboo split, where only a limited vocabulary with a lot of syntactical variety is present.
The datasets were following: ATIS (Hemphill et al.. 1990). Liu (Liu et al., 2021), Face- book (Gupta al., 2018), Snips (Coucke et al., 2018) and CLINC150 (Larson al., 2019b). The intent domains and coding schemes varied from dataset to dataset and included restaurants, flight booking, media control, and general knowledge. Only intents included by seed samples were used for evaluation some intents were thus omitted. The seed samples were selected by Larson et al. The total no. samples for each dataset can be found in Appendix C.
In conclusion, when compared to ChatGPT- collected data, the Falcon-collected data yielded more duplicates and invalid samples that resulted in lower quality dataset in terms of lexical di- versity. For this reason, we only use ChatGPT in model robustness experiments described further. However, the performance of open source models is increasing and future models may outperform ChatGPT, giving an interesting outlook for future comparisons.
The paraphrase collection procedure was simi- lar to one of diversity evaluation (section 3), with some key differences. There were 5 sets of seeds (one per each dataset). For each seed set, data was collected in four rounds (instead of three). In each round ChatGPT was provided with the same seed paraphrase. The iterations differed by the number of taboo words (0, 2, 4 and 6). The resulting paraphrases were merged into one final paraphrase dataset (per each of the five benchmark dataset used). Empty responses, duplicates and
4 Model robustness
To compare the robustness of models trained on ChatGPT and human paraphrases (RQ3), computed accuracy of these models over out-of- distribution (OOD) data. The comparison indicates higher or comparable accuracy of ChatGPT-data- trained models.

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paraphrases with taboo words were filtered our As previously, we manually checked the validity of all paraphrases.
all of the experiments the authors of the original study estimate the cost of one paraphrase be at $0.05. The pricing of the API during the collection of these samples was $0.002 per 1K tokens.
From now on, we denote the crowdsourced para- phrases from the study of Larson et al (Larson et al., 2020) as the human data. We denote the ChatGPT collected data as the GPT. The OOD test data (the "non-seed" remainder of the original 5 datasets) are denoted as the origi irriiaal data. Figure 2 illustrates the evaluation process for one benchmark dataset.
For diversity experiments, the number of col- lected paraphrases from the original study is 10,050 samples. This results in approximate cost of the original study approximately $500. We have col- lected a total of 9,750 samples from ChatGPT. This resulted in a total cost of our data collection experi- ments of approximately $0.5. This is a 1:1000 ratio for ChatGPT when both studies are compared.
We have also checked for possible overlaps be- tween GPT, human and original data for all the datasets to check if crowdsourcing workers or Chat- GPT have not generated the same sentences as are included in the original data. We have detected less then 1 1% of collected data to be overlapping and have removed it from the experiments. The results and details can be found in the Appendix
For the model robustness experiments, the to- tal data size from the original study for all the 5 benchmark datasets combined 13,680 samples, which corresponds to an approximate cost of the original study at $680. In our experiments, we have collected 26,273 samples for $2.5. This results in an approximate ratio of 1:525 in favor of ChatGPT. Together, both experiments make up the price ratio of 1:600 for ChatGPT. We conclude that the price of using ChatGPT for collecting paraphrases for intent classification much lower than with crowdsourcing.
4.2 Accuracy
data We evaluate our experiments for two models: we fine-tuned BERT-large 6 for 5 epochs using the huggingface library and we fine-tuned the SVM 7 multiclass classifier with TF-IDF features using the standard sklearn implementations.
We report our results for BERT in Table while the results for the SVM classifier can be found in the Appendix F. As the results show, models trained on ChatGPT (GPT) generated datasets achieve com- parable results to models trained on human data when evaluated on the OOD original data in 2 cases (Liu and Snips) and outperform the mod- els trained on human data in 3 cases (Facebook, CLINC150 and Snips datasets). Similar effects can be observed for the SVM classifier well. This in- dicates that models trained on GPT data have equal (or in some cases better) robustness than models trained on human data for the intent classification task. Full results for models trained on each dataset can be found in Appendix F.
Discussion
Given the results our experiments, we perceive ChatGPT as a viable alternative to human para- phrasing. However, we also will not argue for a complete replacement yet (for that, many more in- vestigations are needed). ChatGPT, while generally powerful, still has some weaknesses.
During the manual validation of ChatGPT gener- ated data we noticed that ChatGPT does not change named entities for their acronyms (or vice versa) or for their other known names. For example, the word 'NY", even though it refers to 'New York' in the context of the sentence, will always remain the same in all the paraphrases generated by ChatGPT. This would indicate that while ChatGPT is capable of producing very rich sentences in terms of lexical and syntactical diversity (as seen in Section 3.3), does not produce alternative names for named entities, e.g. locations, songs, people, which is something crowdsourced data handles well.
5 Cost comparison: ChatGPT vs crowdsourcing?
We compare the crowdsourcing costs of the original study with our ChatGPT replication and we do this for both the diversity experiments and for the model robustness experiments on OOD data. In
The tendency of ChatGPT to produce "odd" para- phrases also has potential implications: the diver- gence from typical human behavior can harm many applications. On the bright side, oddity can indi- cate casier machine-aen generated text detection, which may serve (crowdsourcing) fraud detection.
Wee have also investigated the effects of including the taboo paraphrases, see Appendix
'https: /haggingfaa co/bert-Beeeee Setert-Large-uncsse https://scikitt-leam orrrrnoorrrraroooo org/stable e/modules/ senerated/sklearn.ssv html
machine-generated

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Original dataset
Data collection
Training data
Finetune BERT
Seed datá...
Human data collection
Human dataset
BERT-large
Evaluation dafa,
ChatGPT collection
ChatGPT dataset
performance
(OOD) data for one dataset on BERT-large, same process was repcated for SVM. This process has been repeated for all the datasets for a comparison of robustmess for models trained ChatGPT and human collected data.
Figure 2: The evaluation process
FB
ATIS
Liu
CLINC150
Snips
# samples
3092
2302
1140
3019
4216
Human
accuracy
72.65(124)
79.46(1.83
93.81
95.42(1.04)
98,891a23)
conf. interval
[71.41-73.89]
[78.33-80.59]
[93.02-94.59]
[94.59-96.26]
[98.72-99.08]
samples
2976*
2210*
1133
3194*
3961"
GPT
accuracy
76.53x27D)
87.64
93.55
98.06
99.13xo.1))
conf. interval
[74.85-78.21]
(85.62-89.67]
(93.21-93.88)
(97822-9.29)
(98.98-99.27)
Table 3: Accuracy of a BERT language models over 10 runs, finetuned for the task of intent classification for different datasets on the GPT data and human data from the original study. Values in brackets near the mean denote the standard deviation. We also report the number of samples per each dataset for each dataset, with the asterisk meaning that dataset was downsampled and the 95% confidence intervals for accuracy. Models trained on GPT data have better robustness OOD data in 3 cases and comparable robustness 2 cases when compared models trained on human data.
As our results also show, models trained on GPT data do not always perform better than those trained on human data. should be noted that the OOD test data is imbalanced between labels in the case of FB, ATIS and Liu, although the number of samples for each label in the training data (both GPT and human) is balanced for each of the 5 datasets. This perfor- mance might be due number of factors. First, the number of samples used for training in the L Liu dataset is the smallest of all datasets, which might result distribution bias where not enough data was collected to have better comparison between models trained on GPT and human data. Second, the lack of paraphrases or alternative names for named entities in the GPT data might result in poorer per- formance if the test data contain many alternative names. Third, there may be other hidden biases in the datasets.
up with human paraphrasers and overseers.
7 Conclusion
In this work, we compared the quality of crowd- sourced and LLM generated paraphrases in terms of their diversity and robustness of intent classifica- tion models trained over them. We reused the data collection protocol of the previous crowdsourcing study of Larson et al. (2020) to instruct ChatGPT and Falcon-40B instead of crowd workers. Our re- sults show that ChatGPT collected data yield higher diversity and similar model robustness to the data collected from human workers for fraction of the original price, while Falcon-40B struggled in cre- ating valid and unique paraphrases. The effects of human-collected and CharGPT-colleeter data on robustness vary, which might indicate the need for their combination for best model performance.
All of these observations indicate that ChatGPT is able create diverse paraphrases both in terms of their structure and vocabulary, but with certain limitations. Given the current state of our knowl- edge, we see ChatGPT as a paraphrase creator es- pecially for data augmentation scenarios, teamed
The much cheaper ChatGPT data collection pro- vided with a better dataset (in many aspects). Given our observations, it does appear beneficial to us use ChatGPT as a supplement crowd- sourcing for paraphrase collection. We do not rule out the use of human computation for this task, but

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the same time, we speculate that the usage of ChatGPT as a data enhancement method might be beneficial for dataset building.
Sixth, we have not further investigated the source of the mixed results in Section and have only speculated at the source of these uneven results. Seventh, the reproducibility of our data collec- tion process is dependent upon the owners of Chat- GPT services the models get further finetuned with new data and other changes are made to them without the models themselves being public. This means that there is no guarantee that our study can be reproduced an exact manner in the future. Eigth, the performance of Balcon-40B-instruce in Section 3.4 could be enhance via further fine-tuning of the model, possibly yielding better results.
Our results lead to new questions about Chat- GPT's capabilities for the creation of paraphrases. A possible point of diminishing returns for this paraphrasing task in ChatGPT might be explored in order to determine when too many duplicates are created from ChatGPT. The usage of different diver- sity incentives on ChatGPT could further improve the performance of ChatGPT. Another area of in- terest might be the usage of ChatGPT paraphrasing for low-resouree languages, where further investi- gation to the quality of created data is needed.
Ninth, the good results for model robustness on OOD data of ChatGPT-collecter data can be at- tributed to the data which ChatGPT was trained: possible that ChatGPT has been trained with a corpus containing all the datasets used in our evaluation. Nevertheless, ChatGPT is able to cre- ate lexically and syntactically rich paraphrases that lead to good generalization for models trained on such data while generally avoiding recreating the same sentences that were in those datasets as be seen in Section 4.1.
8 Limitations
There are multiple limitations to our current work. First, we did not estimate when the point of di- minishing returns for ChatGPT paraphrasing hap- pens how many times can we collect paraphrases for the same seed sentence before ChatGPT starts producing too many duplicates. Our experiments show, that taboo words and other similar meth- ods could mitigate this, as the number of dupli- cates is much smaller when such restrictions are used. However, it might be the case that a large enough crowd could beat ChatGPT in paraphras- ing for the same seed in terms of diversity, most probably when ChatGPT starts repeating itself. Second, ChatGPT does not paraphrase or use alternative names for named entities such as loca- tions, people, songs, etc. This might be mitigated with prompt engineering, but this currently limits its paraphrase outputs in cases where named enti- ties are present in seed sentences.
Ethies Statement
We see ethical concerns pertaining directly the conduct of this research. In our study, we an- alyzed existing data or data generated using Chat- GPT API. Albeit production of new data through LLMs bears several risks, such as introduction of biases, the small size of the produced dataset, suf- ficient for experimentation is, at the same time, insufficient for any major machine learning endeav- ors, where such biases could be transferred.
Third, we have not used any specific prompt en- gincering during our work, which might produce better results for ChatGPT, nor have we investi- gated in depth the effects of different API parame- ters on the created paraphrases.
Our findings. of course, highlight concerns for the future: the potential of LLMs replace hu- mans some human intelligence tasks. A threat to crowdsourcing practices as they have been known prior 2022 is already materializing: a recent study of Veselovsky et al. (2023) measured the probable use of LLMS by crowdworkers in 33-46% cases.
Fourth, we performed experiments for only one language, namely English, while ChatGPT can be used to produce paraphrases in other languages. As such, we do not know what quality of paraphrases would this model produce for different langauges.
A Acknowledgements
This research was partially supported by the Central European Digital Media Observatory (CEDMO), project funded by the European Union under the Contract No. 2020-EU-1A-0267 and has received funding by the European Union under the Horizon Europe vera.ai project, Grant Agreement number 101070093.
Fifth, we have not compared the performance of ChatGPT with other LLMs such as A Alpaca 8 Vicuna 9 or LLaMa (Touvron et al., 2023).
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pre-training

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A Example code for sending requests to the ChatGPT API
We have collected paraphrases from ChatGPT us- ing similar code as below (the same API parameters were used in our data collection): def
Brian Thompson and Matt Post. 2020. Paraphrase gener- ation as zero-shot multilingual translation: Disentan- gling semantic similarity from lexical and syntactic diversity. In Proceedings of the Fifth Conference on Machine Translation, pages 561-570, Online. Asso- ciation for Computational I Linguistics.
request_ grspess_rrsposeeeeo from_ got (prompt): response openai. ChatCompletion. create(
model="gpt-3.0 -.. 5-turo"",,
messages=[
("role": "system", "content"; "You are a crowdsourcing worker that earns a living through creating paraphrases. "). ("role": "user", "content"; prompt }],
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Aner Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Fric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models.
temperature=1,
peequency_pemalty-e
presence__enaltyyll.5 n=13)
Petter Törnberg. 2023.
ChatGPT-4 Outperforms Experts and Crowd Workers Annotating Polit- ical Twitter Messages with Zero-Shot Learning. ArXiv:2304.06588 [es].
return response
B Further visualization of the collected data
Veniamin Veselovsky, Manoel Horta Ribeiro, and Robert West. 2023. Artificial artificial artificial intel- ligence: Crowd workers widely use large language models for text production tasks.
A more in-depth insight into characteristics of the collected datasets (number words and content of

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get_iinfo_road_condittte get_info_route, get_location and paraphrases) is provided by Figure and Figure 4.
CLINC150 dataset an intent classification benchmark with a variety of different intents. 40 intents were sampled and for each intent 10 queries were used as seed data for the data collection pro- cess.
C Datasets used in OOD experiments
This section provides an overview of datasets used in Section 4. We used the same samples and intents as in the original study (Larson et al., 2020) for all the datasets for a direct comparison with human collected data. Samples that were not used as seed samples for the data collection were used during the evaluation as seen in Table 3.
C.1 Dataset statistics for OOD experiments
We report the number of samples in cach dataset after filtering out unrelevant intents and data clean- ing as per Section 4. In our experiments we always downsampled datasets each run to adjust for the different number of samples in each dataset.
Facebook dataset is an intent classification and slot filling dataset with intents about interaction with a virtual assistant (Gupta et al., 2018). As per the original study (Larson et al., 2020), we used 10 intents and for each intent 30 queries were seed samples for the data collection it- self. The used intents were: get_directions, dit_aist get_estiimated_ get_estiimateddduratisi
For the Liu dataset experiments the human data contains 1 140 samples, the GPT data contains 2354 with taboo samples and 2265 samples without those samples and the or original data contains 4171 sam- ples in the train and 1087 samples in the test split.
For the Facebook dataset experiments the statis- tics are: human data contains 3092 samples, the GPT data contains 6171 with taboo samples and 5937 samples without those samples and the original data contains 19398 samples in the train and 5645 samples in the test split.
get_estimated_arriva
get_info_traaffi.
update_direccions.
For the ATIS dataset experiments the statistics are: human data contains 2303 samples, the GPT data contains 4654 with taboo samples and 4420 samples without those samples and the original data contains 3985 samples in the train and 759 samples in the test split.
Snips dataset is intent classification and slot filling dataset (Coucke et al., 2018) for which, as per the original study, 7 intents were used. For each intent we used 50 samples as seed samples for the data collection. The used intents were: PlayMusic, AddloPlaylist, BookRestaurant GetWeather, RateBook, SearchCreativeNork and SearchScreeningEvent:
For the CLINC1 50 dataset experiments the statis- tics are: human data contains 3019 samples, the GPT data contains 4767 with taboo samples and 4365 samples without those samples and the original data contains 3500 samples in the train and 969 samples in the test split.
ATIS corpus is benchmark dataset (Hemphill al., 1990) used for slot-filling and intent classification with intents related to interaction with a flight booking assistant. Per the orig- inal study, 6 intents were sampled and for each of them 8 to 50 samples were sampled to be used as seeds for the data collection. The used intents were: atis_abbreviation. atis_aircraft, atis_airfare, stis_airline, atis_flight and srdis_ground_serveee
For the Snips dataset experiments the statistics are: human data contains 4126 samples, the GPT data contains 8327 with taboo samples and 7922 samples without those samples and the or original data contains 13615 samples in the train and 697 samples in the test split.
Liu dataset was used as an intent classification benchmark. Intents are similar to the Facebook and Snips datasets and, as the original study, 10 intents were sampled with 10 samples each that were used for the data collection. The used intents were: cooking_recipe datetime_query, news_query.
D
Model robustness on OOD data with the inclusion of taboo samples and model training details
We report the results of OOD evaluation for SVM model with TF-IDF features in Table 4 for both data with and without taboo samples and the results for BERT-large with taboo samples Table The inclusion of taboo samples during training leads to more robust models trained on GPT data.
audio_volume _up. aidio_vollumeddow weather_query, qa_currency, play_music, transport_traffr and music_query.

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nere_count Dit Tasse Tatoe
Tranpe Promp:
0.33
0.30
0.2
0.2
0415
030
0.11-
0.5
0/6
0.92
010
U11 U11 ward crant Ded Edbos l. Table CIPT
UIsLe poan Dai Phoeett Praepe CPPT
0.22
035
632
632
0.00
035
0.5
100
12
Figure 3: A comparison of GPT and human collected data in terms no. words in paraphrases in row and no. unique words in paraphrases in row 2. The GPT GPT-generrted paraphrases are longer and have more unique words.
For BERT finetuning we used 2 or 4 training batch size, 16 or 32 evaluation batch size and le-5 learning rate finetuned for 5 epochs. For training we used a machine with Ix NVIDIA GeForce RTX 3090 24GB GPU, 32 GB RAM and 8 CPU cores. Two training sessions were ran in parallel the same GPU (one with 2 and second 4 training batch size), for total of 10 training runs for each of the 5 datasets.
F
Full results of model robustness for original, GPT and human data on 5 different dataset
We report the full results of models trained on original datasets, GPT data and human data as per Section 4 for each dataset in Table for BERT and in Table 8 for SVM. As can be seen in both Tables, models trained on original data have a considerable drop in accuracy for GPT and human data, while models trained on GPT data achieve the best results in terms of robustness.
E Overlaps between data the 5 different datasets
To determine overlaps between different data (original, GPT and human) on 5 different datasets. we lemmatized all of the texts, casted to lowercase and removed any punctuation. The results can be found in Table 6. GPT data have less overlaps on original than human data have.

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Bhone
aepos
need
Welt
Thela
Check kacckool
number
Know MI
Check
sale
Dakk
ordee
routing
baa
depos
box
box
wathorag
pleasee
Find
iin
a
accountio
poonntenntar et
accounticeec
cneck
Jenoney
enineessa
phooneonee
bOX
hour
number
way Ccount
a
Tel
crose
close
aLtherum
SEcUre
near
(a) Word cloud visualization of the Prompt human dataset.
(b) Word cloud visualization of the Prompt GPT dataset.
Laae
inst institution on
cooed know
money time
Sasste fund
ae Informtiin Lew HITH intitional ya
boakking
order close
out
ing
K
Wha
BOX
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check
bank
payment TödeCashn
OD
teu
PrOCEsS
whats
Pr
ovide
c
Mi
money
a account
find
transaction
Ciecnme
amount
safe dieio number need
muc
inanch
Faa
tell
one
(c) Word cloud visualization of the Taboo human dataset.
(d) Word cloud visualization of the Taboo GPT dataset.
Figure 4: Visualization of different word clouds from collected data. The word clouds from ChatGPT data more dense, with the general same most frequent words, although some differences present (e.g. the word financial Figure 4d.
Test original data (OOD)
Train data split
FB
ATIS
Liu
CLINC1 50
Snips
Human
72.65
79.46
93.81
95.42
98.89
GPT
76.53
87.64
93.55
98.06
99.13
GPT tab. samples
79.64
87.74
93.13
98.42
99.07
Table 4: Accuracy of finetuned BERT-large 10 runs, finetuned for the task of intent classification for different datasets on the ChatGPT collected data with and without taboo samples and human collected data from the original study. The inclusion of taboo samples in training generally leads slightly increased robustness.
Test original data (OOD)
Train data split
FB
ATIS
Liu
CLINC1 50
Snips
Human
66.72
81.79
80.93
87.07
98.62
GPT
60.39
81.12
81.54
94.76
97.98
GPT + tab. samples
64.59
81.81
89.93
95.40
97.85
Table 5: Accuracy of an SVM with TE-IDF features over 10 runs, finetuned for the task of intent classification for different datasets the ChatGPT collected data with and without taboo samples and human collected data from the original study. The inclusion of taboo samples in training generally leads to slightly increased robustness.

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ATIS
FB
Human
Original
Human
Original
GPT
25
1
GPT
10
0
Human
24
Human
15
(b) Overlaps between different different data for the ATIS dataset. The cells denote the no. samples that are in both the data in the row and column.
(a) Overlaps hetween different different data for Facebook dataset. The cells denote the no, samples that are in both the data the row and column.
Liu
CLINC15 50
Human
Original
Human
Original
GPT
10
3
GPT
16
13
Human
8
Human
24
Overlaps between different different data dataset. The vells denote no. samples are both the data the row and column.
Overlaps between different different data the CLINC1 dataset. The cells denote no. samples that in both the the row and column.
Snips
Human
Original
GPT
19
0
Human
12
(e) Overlaps between different different data the Snips datasct. The cells denote no. samples that are both the data the row and column.
Table 6: Overlaps in samples between different data for each of the 5 datasetss used in OOD data experiments. The GPT data have less overlaps on original data than human data.

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Test data
Test data
Train data
GPT
Human
Original
Train data
GPT
Human
Original
GPT
98.65
95.19
87.64
GPT
96.59
91.33
76.53
Human
95.87
96.93
79.46
Human
93.22
94.37
72.65
Original
77.06
65.76
99.89
Original 75.71
73.56
96.60
(h) Results the ATIS dataset.
(a) Results on the Facebook dataset.
Test data
Test data
Train data
GPT
Human
Original
Train data
GPT
Human
Original
GPT
99.16
98.46
93.55
GPT
99.23
93.98
98.06
Human
95.69
97.78
93.81
Human
83.94
96.77
95.42
Original
90.43
93.57
97.13
Original
74.83
82.95
96.46
(c) Results on the Liu dataset.
(d) Results on the CLINC1 50 datasct.
Test data
Train data
GPT
Human
Original
GPT
99.45
96.70
99.13
Human
97.36
98.91
98.89
Original 92.25
94.07
98.78
(c) Results on the Snips dataset.
Table Results each dataset BERT finetuned on differently collected data. report the mean accuracy over 10 runs with different train/test splits each time. The best results for each column (test data) in bold and the second best results are underlined. Models trained on original datasets tend to have the worst results in terms of robustness, while models trained on data have the best results terms robustness.

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Test data
Test data
Train data
GPT
Human
Original
Train data
GPT
Human
Original
GPT
97.76
96.25
81.42
GPT
96.32
92.34
60.39
Human
94.09
97.18
81.79
Human
87.33
95.23
66.72
Original
34.14
38.98
91.96
Original
38.39
58.71
94.65
(h) Results on the ATIS dataset.
Results the Facebook dataset.
Test data
Test data
Train data
GPT
Human
Original
Train data
GPT
Human
Original
GPT
97.61
93.08
81.54
GPT
96.24
88.44
94.76
Human
85.78
94.87
80.93
Human
75.33
93.44
87.07
Original
51.92
63.47
91.71
Original
45.31
58.43
92.88
(c) Results the Liu dataset.
(d) Results on the CLINC150 150 datasct.
Test data
Train data
GPT
Human
Original
GPT
99.68
99.15
97.99
Human
97.49
99.56
98.62
Original
68.10
85.42
97.99
(e) Results the Snips dataset.
Table Results for each dataset for SVM trained with TF-IF features on differently collected data. We report the mean over 10 runs with different train/test splits each time. The best results for each column (test data) are bold and we underline also the second best results. Similar to BERT, SVM models trained on or original datasets tend to have worst results in terms of robustness, while models trained on data the best results terms robustness.


Reviews:
[{"review_id": "p8OEM0cIDX", "reviewer": "Reviewer_HLjE", "summary": "", "questions": "Is there a reason you chose to focus on paraphrase generation specifically?\n\nHow does using ChatGPT compare with paraphrases generated using other LLMs such as GPT-3 and open-source models like Falcon and Vicuna?\n\nHave you tried other diversity-encouraging methods other than taboo words?\n\nHave you tried using ChatGPT itself for intent classification? Is there really a point in generating additional paraphrases for it if the model itself may be strong enough already at the end task?", "limitations": "", "rating": 4, "confidence": 3, "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": "Thank you very much for your assessment. Below, we address each question (or critical remark) individually.\n\n> The authors only focus on paraphrase generation. It would have been nice to see a bit broader set of use cases or tasks.\n\n> Is there a reason you chose to focus on paraphrase generation specifically?\n\nIn our research, we are interested in dataset augmentation (for increasing model robustness through better data). Specifically, we are interested in creation of adversarial and out-of-distribution samples used for training. In this scope, paraphrasing is one of the most used methods. We were originally investigating human computation (crowdsourcing) and human-in-the-loop methods. The advent of ChatGPT and LLMs prompted us to investigate their capabilities in paraphrasing. However, we didn’t build the paper's motivation following this line. Instead, we felt that investigating the impact of LLMs on crowdsourcing is more straightforward (and more important).\n\nOf course, the capabilities of ChatGPT to solve other typical crowdsourcing tasks should be investigated, but this remains a future work as it would be a bit out of scope of this paper. We deem it as a fruitful challenge with lots of research opportunities: for every crowdsourcing task, one needs a corresponding set of reference human-originating data. Some suitable crowdsourcing studies (like of Larson et al) addressing other tasks probably could be identified, however, many more would require additional crowdsourcing in order to systematically map the field. We hope that more studies like ours will be commenced in the future.\n\n> Further, they do not compare the generated paraphrases to other LLMs such as GPT-3 and open-source models like Falcon and Vicuna, which would shed additional insights into 1) how much better ChatGPT and GPT-4 are than these other LLMs, 2) how much is it worth paying the OpenAI API costs compared to using open-source models.\n\n> How does using ChatGPT compare with paraphrases generated using other LLMs such as GPT-3 and open-source models like Falcon and Vicuna?’\n\nThe focus of our current paper was to compare the crowd vs. some widely known and used LLM to investigate if such LLMs could pose a threat to crowdsourcing by producing high quality (valid and diverse) paraphrases. We have therefore selected the most popular and accessible LLM at the time - ChatGPT. As its performance outperformed humans in our experiments, it sufficiently answered our research questions (without the need of investigating further LLMs). However, investigation of paraphrasing capabilities of other LLMs is a logical step to take and a future work for us. \n\nPrompted by the review(s), **we conducted new experiments with both Falcon-7B-instruct and Falcon-40B-instruct** (highest ranking models on the Huggingface benchmark at the time of submission of the paper). We will incorporate the results into the final version of the paper.\n\nWe used the same setup as in the diversity experiments - replicating the method of Larson et al. 2020 for collecting paraphrases for 10 different financial intents. We did not conduct any specific parameter tuning, used the same parameter values as for ChatGPT and used the same prompts as we did for the ChatGPT experiments.\n\nWe preprocessed the data - removed duplicates and for *taboo* split removed paraphrases which contained tabooed words. Next, as we did not have time to fully validate ~5.5k paraphrases, we randomly chose 600 paraphrases for each split per model to be annotated for validity (is this a valid paraphrase given intent and seed sentence?). The 4 annotated datasets were Falcon-7B prompt and taboo; and Falcon-40B *prompt* and *taboo*. The randomly chosen 600 paraphrases per dataset were annotated by one of the authors. \n\nSummary of our findings:\n\n- Both Falcon models struggled with producing a unified format for the paraphrases, resulting in a some additional overhead in parsing the results;\n- Both Falcon models struggled to produce paraphrases and follow the basic instructions at times.\n- For the Falcon-7B collected *prompt* data, 92 out of the 600 (15.33%) sampled paraphrases were invalid and for the *taboo* data it was 161 (26.83%) invalid paraphrases; in most cases, the “invalidity” was caused by a semantic mismatch between the paraphrase and the seed.\n- Falcon-40B *prompt* data had 103 (17.17%) invalid paraphrases and for the *taboo* it was 177 (29.5%) invalid paraphrases; mostly due to the model treating the paraphrasing task like a conversation, answering to the seed sentence rather than paraphrasing it, thus ignoring the instructions\n\n**Overall, Falcon-collected outcomes did not match the quality of ChatGPT’s.** Compared to ChatGPT, where no invalid paraphrases were detected during our manual evaluation, the Falcon-collected outputs require a lot more manual validation and filtering.\n\nIn the paper itself, we plan to use the additional page to highlight this comparison of open source LLMs vs. ChatGPT for this task with further explanation of methodology and discussion of additional results after our manual validation is done. The additional results will entail: lexical diversity, syntactical diversity, no. valid samples and no. samples with wrong paraphrases per each Falcon model variation.\n\n> Also, it would have been nice to see the authors try training models such as autoregressive LLMs and not only BERT-large and SVM.\n\nWhile this was not the focus of our paper, it is an interesting topic for future work. In our paper, we kept the selection of the classifiers the same as in Larson et al. 2020 as we were more interested in the relative comparison of crowd and ChatGPT and less in achieving the best possible classifier performance. However, future work should indeed try to maximize downstream task performance (including experimentation with other classifiers, but also generator LLMs).\n\n> It would have been nice to see the authors attempt to compute their own taboo words based on ChatGPT paraphrases from previous rounds, rather than using the taboo words from existing work.\n\nAs suggested, we have considered using taboo words based on ChatGPT paraphrases from previous rounds (either instead or along with the original taboo words). However, in the end we stuck with reusing the ones from Larson et. al to strengthen the comparison. We dismissed using both in order to keep the study simple.\n\n> Have you tried other diversity-encouraging methods other than taboo words?\n\nSimilarly, as with the classifier model selection, we have opted for taboo words only because we were replicating the original study and were pursuing a direct comparison between the performance of crowd and ChatGPT. At the same time, taboo words are the most widely used technique for human computation scenarios (per related work). Of course, the exploration of other options is relevant to future work, especially due to the possibility that LLMs may process the diversity cues differently than humans.\n\n> Lastly, there is a bit of circular logic here. I have a strong suspicion that ChatGPT may be strong enough at intent classification itself. Hence, rather than generating additional paraphrases to train other intent classification models, using ChatGPT may be enough. I understand the point of this paper is to test the ability of ChatGPT to generate additional paraphrases to train other models for tasks such as intent classification, but when ChatGPT itself is likely good enough for most end tasks, I question the value of this - especially the way it's framed in this paper, which is to use the generated paraphrases to train intent classification models.\n\n> Have you tried using ChatGPT itself for intent classification? Is there really a point in generating additional paraphrases for it if the model itself may be strong enough already at the end task? \nInvestigating the ability of LLMs to perform intent classification is a relevant future work, yet outside of the scope of our current paper. Using an LLM for a downstream task directly makes sense in many cases (it is straightforward and can be done in zero-shot or in-context-learning setup). At the same time, using LLMs as training set generators opens up possibilities to create cheaper (lesser) classifiers and allows more quality control over the data.\n\nThat is why the actual downstream task is not our main focus in this paper. Our real concern is the quality of the created paraphrases (as we mention above, our research focus and research questions are motivated by crowdsourcing and its future in the age of accessible LLMs). That is why our experiments are not strictly focused on intent classification: our prompts and prompting techniques have nothing to do with the specific task of intent classification and can be used for any paraphrasing (similar to the original study, from which we reuse the prompts). Of course, it might be the case that ChatGPT can create such good paraphrases because it can implicitly detect the intent.", "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": "The paper is a valuable contribution as it helps shed light on an important question: whether ChatGPT can be used to replace crowdsourcing, particularly for obtaining paraphrases. This is important going forward, as ChatGPT will likely be used more and more as a source for acquiring data.\n\nThe data collection and experiments are well planned and executed, following prior methodology (e.g. 3-round approach) for data collection. There is a decently extensive analysis of the generated paraphrases in terms of diversity, validity, and other characteristics.\n\nLastly, there is a detailed and comprehensive limitations section, showing that the authors have reflected heavily on the limitations and potential future improvements of their work.", "reasons_to_reject": "The authors only focus on paraphrase generation. It would have been nice to see a bit broader set of use cases or tasks.\n\nFurther, they do not compare the generated paraphrases to other LLMs such as GPT-3 and open-source models like Falcon and Vicuna, which would shed additional insights into 1) how much better ChatGPT and GPT-4 are than these other LLMs, 2) how much is it worth paying the OpenAI API costs compared to using open-source models.\n\nAlso, it would have been nice to see the authors try training models such as autoregressive LLMs and not only BERT-large and SVM.\n\nIt would have been nice to see the authors attempt to compute their own taboo words based on ChatGPT paraphrases from previous rounds, rather than using the taboo words from existing work. On a related note, it would have been nice to see other diversity-encouraging methods other than just taboo words.\n\nLastly, there is a bit of circular logic here. I have a strong suspicion that ChatGPT may be strong enough at intent classification itself. Hence, rather than generating additional paraphrases to train other intent classification models, using ChatGPT may be enough. I understand the point of this paper is to test the ability of ChatGPT to generate additional paraphrases to train other models for tasks such as intent classification, but when ChatGPT itself is likely good enough for most end tasks, I question the value of this - especially the way it's framed in this paper, which is to use the generated paraphrases to train intent classification models.", "questions_to_authors": "Is there a reason you chose to focus on paraphrase generation specifically?\n\nHow does using ChatGPT compare with paraphrases generated using other LLMs such as GPT-3 and open-source models like Falcon and Vicuna?\n\nHave you tried other diversity-encouraging methods other than taboo words?\n\nHave you tried using ChatGPT itself for intent classification? Is there really a point in generating additional paraphrases for it if the model itself may be strong enough already at the end task?", "paper_topic_and_main_contributions": "This paper assesses the ability to use ChatGPT to replace crowd workers in paraphrase generation, specifically for training intent classification models. They follow a 3-round data collection methodology following prior works using taboo words to encourage diversity, and find that ChatGPT generates more diverse data (both lexically and syntactically). The authors train BERT and SVM models using the generated paraphrases and look at the accuracy on OOD data, and find that they achieve comparable/better results on most datasets compared to models trained on the original human data.", "excitement": 4, "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": {}}, {"review_id": "1gQ2O3Q7w0", "reviewer": "Reviewer_YaLe", "summary": "", "questions": "Q1: In 4.1 last paragraph of page 5, it is discussed that the possible overlaps are checked. Is this the overlap between GPT, human, and original data for cross-set bias between train and test? This is unclear.\n\nQ2: A concern with TED calculations. I appreciate the normalization between intents, however if the intents can be vague, which would make the normalization less appropriate. To elaborate, how detailed are the intents or specific acts? The examples in Table 2 seem to be somewhat specific, which is good for any takeaways regarding TED. So, if intent \"phone\" refers to many entities or actions such as texting, calling, using apps, the device itself, then the TED values lumped together would not really tell us how diverse the responses are, simply that there are many diverse actions that are lumped into an intent.\n\nQ3: How did you manual validate the ChatGPT paraphrases? It is hard to believe that over 10,000 statements were manually validated. Just some details suffice.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your assessment. Below, we address each question or critical remark individually.\n\n> Q1: In 4.1 last paragraph of page 5, it is discussed that the possible overlaps are checked. Is this the overlap between GPT, human, and original data for cross-set bias between train and test? This is unclear.\n\nThese overlaps have been computed on the entirety of the GPT, human and original data, without distinction between train and test data. We understand that this is not mentioned in the text and we will fix this in the camera ready version.\n\n> Q2: A concern with TED calculations. I appreciate the normalization between intents, however if the intents can be vague, which would make the normalization less appropriate. To elaborate, how detailed are the intents or specific acts? The examples in Table 2 seem to be somewhat specific, which is good for any takeaways regarding TED. So, if intent \"phone\" refers to many entities or actions such as texting, calling, using apps, the device itself, then the TED values lumped together would not really tell us how diverse the responses are, simply that there are many diverse actions that are lumped into an intent.\n\nWe will clarify the TED calculations (also  will be explained more clearly in the paper for the camera ready version): TED is first calculated pair-wise for each phrase in the intent and data split (separate for human, GPT, original). This is then averaged to get the mean – this should represent how syntactically diverse the phrases are - the higher the number of mean TED is, the more diversity is present.\n\nThe issue of having “too general” intents in the data could potentially have the same effect on the lexical diversity – it would only tell us that we have diverse actions lumped under one intent. This is an issue of the existing datasets, where some intents can be too general, while others are overly specific. We, however, have not noted any such overly general intents in the 5 datasets which we used, so the computations of TED and lexical diversity should not be affected by the phenomenon mentioned by the reviewer. E.g. The intent ‘phone’ simply refers to one action, namely calling the banking institution in the financial dataset and not other phone communication actions.\n\nWe will clarify the intents used in our experiments by adding a Table of used intents for each dataset in the Appendix for the camera ready version together with examples and clarifications about each intent.\n\n> Q3: How did you manual validate the ChatGPT paraphrases? It is hard to believe that over 10,000 statements were manually validated. Just some details suffice.\n\nTo validate the created paraphrases we used a simple web application that we developed for this task. The user, who was one of the authors, was shown the seed samples, from which ChatGPT generated the paraphrases, and one particular paraphrase to validate. The author was then able to either mark the paraphrase as valid or not, with an additional optional checkbox to label the paraphrase as ‘borderline case’ for possible revisions. As the seed sentence changed only once in a while (one time for each 5-20 paraphrases) this significantly reduced the cognitive load on the annotator. Even with such reduction, the entire validation process took a few days (less than a week), as we also discussed the ‘borderline cases’ where the user was not sure about the validity of created paraphrases.\n\nIn the camera ready version, we will add details about this process into the paper.\n\n> The generalization of model robustness findings should be tested in other models and architectures. Given the code provided, future work is likely doable in intent classification and other applications.\n\nExperimenting with more generators and classifiers is a limitation that we hope to address in the future work. Fortunately, it didn’t prevent us from answering our research questions, which aimed at comparing the performance of the crowd vs. some widely known and used LLM to investigate if such LLMs could pose a threat to crowdsourcing by producing high quality (valid and diverse) paraphrases. The performance of ChatGPT was, in our opinion, so convincing that we did not need to compare the crowd to other LLMs. However, investigation of paraphrasing capabilities of other LLMs is a logical step to take and a future work for us. \n\nPrompted by the review(s), **we conducted new experiments with both Falcon-7B-instruct and Falcon-40B-instruct** (highest ranking models on the Huggingface benchmark at the time of submission of the paper). We will incorporate the results into the final version of the paper.\n\nWe used the same setup as in the diversity experiments - replicating the method of Larson et al. 2020 for collecting paraphrases for 10 different financial intents. We did not conduct any specific parameter tuning, used the same parameter values as for ChatGPT and used the same prompts as we did for the ChatGPT experiments.\n\nWe preprocessed the data - removed duplicates and for *taboo* split removed paraphrases which contained tabooed words. Next, as we did not have time to fully validate ~5.5k paraphrases, we randomly chose 600 paraphrases for each split per model to be annotated for validity (is this a valid paraphrase given intent and seed sentence?). The 4 annotated datasets were Falcon-7B prompt and taboo; and Falcon-40B *prompt* and *taboo*. The randomly chosen 600 paraphrases per dataset were annotated by one of the authors. \n\nSummary of our findings:\n\n- Both Falcon models struggled with producing a unified format for the paraphrases, resulting in a some additional overhead in parsing the results;\n- Both Falcon models struggled to produce paraphrases and follow the basic instructions at times.\n- For the Falcon-7B collected *prompt* data, 92 out of the 600 (15.33%) sampled paraphrases were invalid and for the *taboo* data it was 161 (26.83%) invalid paraphrases; in most cases, the “invalidity” was caused by a semantic mismatch between the paraphrase and the seed.\n- Falcon-40B *prompt* data had 103 (17.17%) invalid paraphrases and for the *taboo* it was 177 (29.5%) invalid paraphrases; mostly due to the model treating the paraphrasing task like a conversation, answering to the seed sentence rather than paraphrasing it, thus ignoring the instructions.\n\n**Overall, Falcon-collected outcomes did not match the quality of ChatGPT’s.** Compared to ChatGPT, where no invalid paraphrases were detected during our manual evaluation, the Falcon-collected outputs require a lot more manual validation and filtering.\n\nIn the paper itself, we plan to use the additional page to highlight this comparison of open source LLMs vs. ChatGPT for this task with further explanation of methodology and discussion of additional results after our manual validation is done. The additional results will entail: lexical diversity, syntactical diversity, no. valid samples and no. samples with wrong paraphrases per each Falcon model variation.", "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": "- The paper examines previously studied crowdsource work under a new lens. This work brings new insights to crowdsource efforts and the roles LLMs may play in synthetic data generation.\n\n- The experiments regarding lexical and syntactic diversity are interesting. These experiments give insights to the quality of data one can likely see as LLMs are used in place of human crowdsource workers.\n\n- The experiments in OOD appear sound and encouraging. The synthetic data brings measurable improvements in OOD datasets for intent classification in BERT.\n\n- The experiments list confidence internal which shows a significant improvement with little to none overlap in CI. \n\n- Clear experiments with a quality discussion of results\n\n- Limitations of work are thoughtfully discussed.\n\n- The paper contributes data and code that can be used in future works.", "reasons_to_reject": "- The generalization of model robustness findings *should* be tested in other models and architectures. Given the code provided, future work is likely doable in intent classification and other applications.\n\n", "questions_to_authors": "Q1: In 4.1 last paragraph of page 5, it is discussed that the possible overlaps are checked. Is this the overlap between GPT, human, and original data for cross-set bias between train and test? This is unclear.\n\nQ2: A concern with TED calculations. I appreciate the normalization between intents, however if the intents can be vague, which would make the normalization less appropriate. To elaborate, how detailed are the intents or specific acts? The examples in Table 2 seem to be somewhat specific, which is good for any takeaways regarding TED. So, if intent \"phone\" refers to many entities or actions such as texting, calling, using apps, the device itself, then the TED values lumped together would not really tell us how diverse the responses are, simply that there are many diverse actions that are lumped into an intent.\n\nQ3: How did you manual validate the ChatGPT paraphrases? It is hard to believe that over 10,000 statements were manually validated. Just some details suffice.", "paper_topic_and_main_contributions": "*ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness* presents a refresh to a previous study Larson et al. for intent classification using a relatively nascent technology: ChatGPT. The purpose of this study primarily investigates three research questions. Namely, (1) Can ChatGPT generate valid solutions to a paraphrasing task, given similar instructions as crowd workers (from a previous study)? (2) How do ChatGPT and crowd solutions compare in terms of their lexical and syntactical diversity? (3) How do ChatGPT and crowd solutions compare in terms of robustness of intent classification models trained on such data. The paper is motivated by the prevalence of ChatGPT and akin LLMs while noting the expenses and quality concerns of crowdsourcing. This work follows an apples to apples comparison as best as the authors could to compare with Larson 2020. The paper finds that, yes, ChatGPT is a suitable alternative at a fraction of the price for this particular study, and it generates more diverse summaries than the human annotators. The model quality improves on downstream training for intent classification in most datasets in a significant manner, demonstrating that ChatGPT in this study is a suitable replacement on quality alone. Factoring in the cost comparison, the authors make a convincing argument about the replacement of crowdsourced efforts with LLMs.", "excitement": 4, "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": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {}}, {"review_id": "4NUO1JbcKW", "reviewer": "Reviewer_fC6f", "summary": "", "questions": "Question A: How do you design your parameters for using ChatGPT API? Specifically for example, why do you choose the temperature to 1 which means the output is random?\n\nQuestion B: What is the arrow(↑)'s meaning which is shown after `TED` in Table 1?", "limitations": "", "rating": 3, "confidence": 3, "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": "Thank you very much for your assessment. Below, we address each question (or critical remark) individually.\n\n> The investigation in this paper is simple and not deep. \n\nWe tried to keep the investigation methodology simple, yet still sufficient, to answer our research questions (which it did). We believe that the impact of LLMs on crowdsourcing is a new topic and therefore has basic questions open. Furthermore, simple methodologies are easier to reproduce.\n\nIt would be a different situation, if the outcomes of the simple analysis were inconclusive or with marginal effects. Then, more in depth analysis (more metrics, content segmentation or qualitative content analysis) would be relevant. But we believe this is not the case.\n\n> For example, the scope of this paper is limited to the task of intent classification while ChatGPT has a wide range of abilities in crowdsourcing of paraphrases.\n\nOur paper investigates the general ability of ChatGPT to paraphrase to a large degree. The RQ1 (paraphrase validity) and RQ2 (paraphrase diversity) do just that. These RQs (measures) would be relevant for most (if not all) other downstream tasks utilizing paraphrases, not just for intent classification. \n\nThe writing (paper title, abstract, intro) puts a lot of emphasis on the intent classification (to prevent unnecessary over-generalizing interpretations), however, that does not counter the real scope of the paper.\n\nFurthermore, the selection of the downstream task was mostly induced by the existing study which we replicated. From that study, we selected all aspects of the crowdsourcing outcomes that could reasonably be compared with ChatGPT performance.\n\n> Only ChatGPT is used for the investigation while many other open-source LLMs exist. \n\nThe focus of our current paper was to compare the crowd vs. some widely known and used LLM to investigate if such LLMs could pose a threat to crowdsourcing by producing high quality (valid and diverse) paraphrases. We have therefore selected the most popular and accessible LLM at the time - ChatGPT. As its performance outperformed humans in our experiments, it sufficiently answered our research questions (without the need of investigating further LLMs). However, investigation of paraphrasing capabilities of other LLMs is a logical step to take and a future work for us.\n\nPrompted by the review(s), **we conducted new experiments with both Falcon-7B-instruct and Falcon-40B-instruct** (highest ranking models on the Huggingface benchmark at the time of submission of the paper). We will incorporate the results into the final version of the paper.\n\nWe used the same setup as in the diversity experiments - replicating the method of Larson et al. 2020 for collecting paraphrases for 10 different financial intents. We did not conduct any specific parameter tuning, used the same parameter values as for ChatGPT and used the same prompts as we did for the ChatGPT experiments.\n\nWe preprocessed the data - removed duplicates and for *taboo* split removed paraphrases which contained tabooed words. Next, as we did not have time to fully validate ~5.5k paraphrases, we randomly chose 600 paraphrases for each split per model to be annotated for validity (is this a valid paraphrase given intent and seed sentence?). The 4 annotated datasets were Falcon-7B prompt and taboo; and Falcon-40B *prompt* and *taboo*. The randomly chosen 600 paraphrases per dataset were annotated by one of the authors. \n\nSummary of our findings:\n\n- Both Falcon models struggled with producing a unified format for the paraphrases, resulting in a some additional overhead in parsing the results;\n- Both Falcon models struggled to produce paraphrases and follow the basic instructions at times.\n- For the Falcon-7B collected *prompt* data, 92 out of the 600 (15.33%) sampled paraphrases were invalid and for the *taboo* data it was 161 (26.83%) invalid paraphrases; in most cases, the “invalidity” was caused by a semantic mismatch between the paraphrase and the seed.\n- Falcon-40B *prompt* data had 103 (17.17%) invalid paraphrases and for the *taboo* it was 177 (29.5%) invalid paraphrases; mostly due to the model treating the paraphrasing task like a conversation, answering to the seed sentence rather than paraphrasing it, thus ignoring the instructions\n\n**Overall, Falcon-collected outcomes did not match the quality of ChatGPT’s.** Compared to ChatGPT, where no invalid paraphrases were detected during our manual evaluation, the Falcon-collected outputs require a lot more manual validation and filtering.\n\nIn the paper itself, we plan to use the additional page to highlight this comparison of open source LLMs vs. ChatGPT for this task with further explanation of methodology and discussion of additional results after our manual validation is done. The additional results will entail: lexical diversity, syntactical diversity, no. valid samples and no. samples with wrong paraphrases per each Falcon model variation.\n\n> The influence of parameters of using ChatGPT for this task is not profoundly investigated.\n\n> Question A: How do you design your parameters for using ChatGPT API? \n\nWe have investigated several existing works to design the parameter setup. We also kept default values, where related work didn’t advise otherwise. The outcomes of the experiments did not required alterations of the parameter setups w.r.t. research questions. \n\n>Specifically for example, why do you choose the temperature to 1 which means the output is random?\n\nThe Temperature parameter is mostly referred to as the one controlling ‘randomness’, ‘diversity’ and ‘creativity’ of the output.  In our research, we wanted to maximize diversity and therefore went with the highest Temperature setting. As the resulting paraphrases were valid and were adhering to the seed sentences and intents, we did not consider other Temperature settings.\n\n>Question B: What is the arrow(↑)'s meaning which is shown after TED in Table 1?\n\nThe meaning of the symbol is that the higher values are considered better. We will add an explanation to the table caption.\n\n>The writing of this paper is a bit colloquial. The tense of some sentences is inconsistent. For example, line 281, \"We also observed that ChatGPT ...\": Past Tense line 333, \"We also compare ...\": Present Tense\n\nFor the camera ready version, we will do a thorough check on the language, including tense harmonization.", "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. This paper shows the ability of ChatGPT to yield higher diversity and similar model robustness than humans in collecting data for intent classification. \n2. This paper leads to new questions about ChatGPT's capabilities for the creation of paraphrases.", "reasons_to_reject": "1. The investigation in this paper is simple and not deep. For example, the scope of this paper is limited to the task of intent classification while ChatGPT has a wide range of abilities in crowdsourcing of paraphrases. Only ChatGPT is used for the investigation while many other open-source LLMs exist. The influence of parameters of using ChatGPT for this task is not profoundly investigated.\n2. The writing of this paper is not very clear. Some of the unclear details are explained in the question.", "questions_to_authors": "Question A: How do you design your parameters for using ChatGPT API? Specifically for example, why do you choose the temperature to 1 which means the output is random?\n\nQuestion B: What is the arrow(↑)'s meaning which is shown after `TED` in Table 1?", "paper_topic_and_main_contributions": "This paper empirically studies whether ChatGPT can replace the crowdsourcing of paraphrases for intent classification. Through replicating the existing study (Larson et al., 2020) with ChatGPT rather than human workers, this paper finds the ability of ChatGPT to yield higher diversity and similar model robustness than humans in collecting data for intent classification.", "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": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "The writing of this paper is a bit colloquial. The tense of some sentences is inconsistent.\nFor example, \nline 281, \"We also observed that ChatGPT ...\": Past Tense\nline 333, \"We also compare ...\": Present Tense"}}]

Author rebuttal:

Meta-review: The paper in question aims to evaluate the ability of ChatGPT to replace crowdsourced human workers in generating paraphrases for training intent classification models. 

Pros: 
1. All the reviewers acknowledge that the paper addresses a relevant and important topic concerning the potential of ChatGPT as an alternative to traditional crowdsourcing methods for obtaining paraphrases.
2. The study adopts a data collection methodology consistent with previous studies, providing a basis for comparison. The detailed analysis of generated paraphrases on parameters like diversity and validity adds credibility to the findings.
3. There is a thorough examination of the limitations of the study, suggesting the authors' depth of understanding and potential areas of improvement.

Cons:
1. Limited Scope: Reviewers also point out that the focus remains solely on paraphrase generation. A broader range of tasks would have offered more comprehensive insights.
2. The omission of a comparison between ChatGPT and other open-source LLMs, like Falcon, Vicuna, is required by reviewers. I can see that the authors add new experiments with Falcon-7B-instruct and Falcon-40B-instruct during rebuttal. These new results should be added to the camera-ready version.
Decision: Accept-Main