Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation (2024)

Abstract: The latest generative large language models (LLMs) have found their application in data augmentation tasks, where small numbers of text samples are LLM-paraphrased and then used to fine-tune downstream models. However, more research is needed to assess how different prompts, seed data selection strategies, filtering methods, or model settings affect the quality of paraphrased data (and downstream models). In this study, we investigate three text diversity incentive methods well established in crowdsourcing: taboo words, hints by previous outlier solutions, and chaining on previous outlier solutions. Using these incentive methods as part of instructions to LLMs augmenting text datasets, we measure their effects on generated texts’ lexical diversity and downstream model performance. We compare the effects over 5 different LLMs, 6 datasets and 2 downstream models. We show that diversity is most increased by taboo words, but downstream model performance is highest with hints.

heading: 1 Introduction
text: The emergence of large language models (LLMs) such as GPT-4, LLaMA, etc., has sparked interest in using them to augment textual datasets (Ubani et al., 2023; Dai et al., 2023; Piedboeuf and Langlais, 2023). In these scenarios, the number of samples is expanded by paraphrasing existing ones through LLM prompting. The created paraphrases are then added to the original dataset and used for downstream model training. Such methods have been explored for various domains such as sentiment classification (Piedboeuf and Langlais, 2023; Ubani et al., 2023), news classification (Piedboeuf and Langlais, 2023) and health symptoms classifications (Dai et al., 2023). However, investigation of the effect of various prompts, specific instructions, and selection of seed data inspired by crowd in the text augmentation process when using LLMs is lacking.
Crowdsourcing is an established practice for collecting training or validation examples for a variety of NLP tasks. Scenarios of data collection using human workers can be similar to those of data augmentation: workers create paraphrases on existing sentences chosen from a dataset. The aim of such data collection is to increase the data diversity and subsequent performance of classifiers trained on the data (Larson et al., 2019, 2020). To increase the diversity, various methods are used in crowdsourcing to guide workers. These include taboo words (Larson et al., 2020) - where most significant words from the collected data are identified and listed in the worker instructions to be avoided during paraphrasing, chaining (Rhys Cox et al., 2021; Larson et al., 2019) - where outliers in the previous paraphrases are identified and used as seed sentences in the next round of data collection, and hints where previous outlier paraphrases are used as
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examples in the instructions. The hints (Rhys Cox et al., 2021; Zhou and Bhat, 2020) method itself is similar to LLM in-context learning, where examples are included in the instructions for the model to achieve better performance. All of these diversity incentive methods report increased diversity of paraphrases and some also report increased performance of the classifiers trained on the so-collected data.
This work is inspired by the parallels between crowdsourcing and LLM prompting and by the performance of diversity incentive methods on the diversity of paraphrases and the performance of models trained on them. We investigate the effects of the three diversity incentive methods (originating in crowdsourcing) on data augmentation using LLMs. The baseline, taken from a previous study (Cegin et al., 2023), is a simple prompting for paraphrases. Measuring paraphrase diversity and downstream performance of classification models, we assess whether the diversity incentives (added to the base prompt) improve LLM outputs similarly as in crowdsourcing scenarios. To our knowledge, this is the first work to investigate the effects of diversity incentive methods on LLMs.
In this paper, we answer the following research questions:
RQ1: Does the usage of diversity incentive methods on LLMs yield more diverse paraphrases? (compared to base prompting)
RQ2: Do classifiers achieve better performance if trained on data augmented using diversity incentive methods on LLMs? (compared to base prompting)
To answer these questions 1, we have conducted a data augmentation experiment using 5 different LLMs on 6 different datasets in the tasks of sentiment (movie and app reviews), news, and intent (flight and voice assistant commands) classification. In this experiment, we repeatedly collect LLM paraphrases using different diversity incentive methods. Then, we compare the lexical diversity of the collected data and the performance of downstream classifiers. Additionally, we also conduct an ablation study, where we modify the diversity incentive methods with random data to validate, that the inputs used by these methods (e.g., most influential taboo words, outlier paraphrases) contribute to the
1Data and code at: https://github.com/kinit-sk/ LLM-div-incts
method’s performance and a combination of the best performing methods for lexical diversity and model performance. In total, we collected 253,500 paraphrases.
The most prominent findings are the following: 1) We do not observe statistically significant improvements in lexical diversity of the generated datasets, but only minor improvements using the taboo method, 2) The hints method increases the performance of classification models trained on such data compared to the baseline, while also reducing standard deviation and thus increasing the stability of results, 3) The chaining method and taboo method both do not significantly affect the performance of classification models trained on such data compared to the baseline.

heading: 2 Related work: Crowdsourcing and LLM-based augmentation

heading: 2.1 Crowdsourcing diverse paraphrases
text: Crowdsourcing of paraphrases is an established method to collect data for dataset building and augmentation in NLP (Larson et al., 2019, 2020; Zhou and Bhat, 2020; Wei et al., 2018; Rhys Cox et al., 2021). In this process, a worker is asked to paraphrase a seed sentence to create new variants (Wei et al., 2018; Larson et al., 2020). To increase the diversity of paraphrases, various instruction variants are used, building on the assumption (shown by (Larson et al., 2020; Joshi and He, 2022; Wang et al., 2022)), that performance of downstream models correlates with training set diversity.
The hints method (Rhys Cox et al., 2021; Zhou and Bhat, 2020) guides workers towards a variety of possible solutions by showing them examples of the most distinct paraphrases previously created by other workers. A variation of this method displays word-clouds of recommended words to be used. Hints have been used for the data collection of user utterances for task-oriented chatbots and to collect diverse motivational messages.
The taboo method (Larson et al., 2020) instructs workers to avoid specific words when paraphrasing. These “taboo words” are drawn from previously collected paraphrases as most influential using a linear SVM. Taboo words have been used in the collection of data for intent classification.
The chaining method (Rhys Cox et al., 2021; Larson et al., 2019) identifies outliers or most distinct paraphrases within the already collected data and uses them as seed sentences. It is applied in
variations for data collection of intent utterances and of motivational messages.
In crowdsourcing, all three methods show increases in the paraphrase diversity and model performance, compared to base prompting.

heading: 2.2 Data augmentation via LLMs
text: LLMs such as GPT-2 (Radford et al., 2019) or BART (Lewis et al., 2020) have previously been used to create paraphrases. Additional extensions used style transfer to create paraphrases of a certain linguistic style (Krishna et al., 2020), syntax control of the generated paraphrases (Goyal and Durrett, 2020; Chen et al., 2020), multi-lingual paraphrases in a zero-shot setting (Thompson and Post, 2020) and LLM finetuning using Low-Rank Adaption for specific domain paraphrase collection (Chowdhury et al., 2022). Recent studies used GPT-3.5 and GPT-4 as data augmentation techniques that were compared with previous stateof-art NLP augmentation techniques (Piedboeuf and Langlais, 2023; Ubani et al., 2023). Two studies report better performance in using LLMs as data augmenters than using previous state-of-art techniques in both paraphrasing existing texts (Dai et al., 2023) and in a zero-shot setup of generating new texts using specific prompts (Ubani et al., 2023). Another study reports mixed results, when GPT-3.5 is compared with previous state-of-the-art techniques (Piedboeuf and Langlais, 2023). Regardless of mixed results, GPT-like models have already been used as augmenters in domains of automated scoring (Fang et al., 2023) and low-resource language generation (Ghosh et al., 2023). However, LLMs can also produce repeating outputs of lower quality (Cegin et al., 2023; Cox et al., 2023).
To our best knowledge, there is no study which investigates if and how diversity incentives (established in crowdsourcing), can be used in LLMsbased paraphrasing. We hypothesize, that use of diversity incentives can prevent the known drawback of LLMs to generate highly similar repetitive content (a challenge that was addressed in crowdsourcing by diversity incentives).

heading: 3 Data collection and evaluation methodology
text: We collected paraphrases for all combinations of the following: 5 different LLMs, 6 datasets, and 3 diversity incentive methods + 1 base prompting. For each combination, 5 collection iterations were
performed: in each 6 random seed sentences per label were drawn from a dataset. For each prompt fired, 5 paraphrases were collected. This totalled in 142,500 collected paraphrases when aggregated all together across datasets and LLMs. For the ablation study and combination of best methods in Section 6 we collected an additional 111,000 paraphrases in total.
As the diversity incentive methods need some previously collected data to determine their cues (hints, seeds or taboo words), each iteration consisted of 2 rounds: first we collected data using only the basic prompt and in the second round, we collected data using the given diversity incentive method (or base prompt method). Thus, the resulting datasets for each method consist of seed data and data collected from both rounds. The entire data collection process is visualized in Figure 1.
After the paraphrases were collected, we evaluated them in several steps. First, we manually checked the validity of a subset (50%) of the collected data (i.e., is the created sample a true paraphrase retaining the label?). Second, we computed the diversity of the collected data, comparing the mean vocabulary size (no. unique words) and mean number of unique 3-grams for each diversity incentive method (refers to RQ1).
Third, we evaluated the performance of models trained on the created paraphrases (refers to RQ2). For each combination of LLM, dataset and method, we finetuned BERT-large 5 times and Mistral-7bv0.1 3 times (the dataset also determined the classification task to which a model was finetuned). We evaluated the accuracy of trained model on the full test set for that given dataset specifically and on a subset of the test set for Mistral to save computational resources following previous works (Chang and Jia, 2023; Köksal et al., 2023; Li and Qiu, 2023; Gao et al., 2021), as the inference time is long and costly. Details of the finetuning process can be found in Appendix D and E.

heading: 3.1 Prompt design
text: As our base prompt, we adopted the instruction design from a previous LLM-paraphrasing study (Cegin et al., 2023). There, the prompt plainly instructs to “Paraphrase this text or sentence 5 times:”, which is followed by the seed sentence.
For the taboo method we take the implementation from (Larson et al., 2020) that uses a linear SVM trained on bag-of-words representation in a one-vs-many setting to identify the 3 most signif-
icant words that are then used in the instructions. We run the computation to get taboo words on the first round of collected data that were collected using the prompt method. We also filter out named entities using NLTK to not include them as taboo words. The prompt used in this method is taken from (Cegin et al., 2023).
For the chaining method we use an outlier detection method from (Larson et al., 2019) where we first compute per label a mean embedding vector from the collected samples from the first round. Then, using Euclidean distance, we find the collected samples that are the furthest away from the mean vector to be used as seed sentences in the second round of data collection. The prompt used in this method is the same as in the prompt method.
The hints method is similar to the previous approach with chaining where we find outliers in the collected data the same way. Here the data are only included in the prompt itself as examples listed with the given seed sentence. The listed examples are always only those that have been created from the given seed to be paraphrased. The prompt is the same as in the prompt method with added delimiter section listing the 3 different hints for the seed sentence.
Templates and examples of the prompts can be found in Appendix H.

heading: 3.2 LLMs used as generators
text: We used 5 different LLMs as data augmenters - 2 open source LLMs, LLaMA-2 and 2 closed LLMs. We chose open LLMs based on their different per-
formance and size on the OpenLLM leaderboard. We used the instruction finetuned versions of the LLMs available at HuggingFace. Namely, for LLaMA-2 (Touvron et al., 2023) we use LLaMA-270B-instruct, for Platypus (Lee et al., 2023) we use Platypus-70B-instruct and for Mistral (Jiang et al., 2023) we use Mistral-7B-instruct. We collected the data on a custom private infrastructure with 16 core CPU, 64 GB RAM and 4xA100 GPUs. As for the closed LLMs, we used 2 of the most widely used: GPT3.5 denoted as ChatGPT (gpt-3.5-turbo-1106 version) and GPT4 (gpt-4-0613 version).

heading: 3.3 Datasets used
text: We used 6 different datasets for our data collection experiments from the domains of news, intent and sentiment classification. We specifically focused on multi-class English datasets as the diversity incentive methods were employed in crowdsourcing processes that used multi-class English datasets. We used the 20 news (Lang, 1995) and AG news (Zhang et al., 2015) datasets for news classification, FB (Schuster et al., 2019) and ATIS (Hemphill et al., 1990) datasets for intent classification and SST-5 (Socher et al., 2013) and Yelp (Zhang et al., 2015) datasets for sentiment classification. We did not use all of the labels in our experiments for the news and intent classification datasets, but randomly select a subset of them. More details can be found in Appendix F.

heading: 3.4 Ablation study setup
text: To investigate if the diversity incetive methods actually influence the diversity of the collected data and performance of classifiers trained on such data we conduct an ablation study. Here, we repeat the data collection process for the open-source LLMs (Mistral, Platypus) and LLaMA-2 using modified versions of each of the diversity incentive method to investigate whether the particular setup of the methods themselves as they have been used in crowdsourcing literature influences the results.
For the taboo method, instead of using the most significant words from the previously generated paraphrases we used 3 random words from the generated paraphrases. For the chaining method and hints method, instead of using the outliers as the next seed sentences or as a hints, we used any previously generated paraphrase that was randomly chosen as seed or hint respectively.

heading: 4 Paraphrase validity and diversity

heading: 4.1 Validity of paraphrases
text: Before evaluating validity of paraphrases, we filtered for malformed phrases, empty phrases or duplicated phrases as per (Cegin et al., 2023). As we collect only 5 samples per one seed sentence, we have detected no duplicated phrases. There were some malformed phrases generated by all LLMs with the exception of ChatGPT, but their number was generally low. The number of collected samples per dataset can be found in Appendix G. The highest amount of mangled or empty paraphrases were detected in GPT-4 responses, mostly when using the chaining method, where the number of invalid paraphrases was approx. 5%. For Mistral, LLaMA-2 and Platypus we detected around 1% of mangled paraphrases. We found no impact of diversity incentive on the number of mangled or empty paraphrases for these LLMs. The detected mangled or empty paraphrases were removed and not included in the next stages.
Second, for each dataset and LLM combination, we sampled 50% of the collected data to be manually validated, i.e. we checked whether the resulting paraphrases are semantically equivalent to the seed sentences and their labels. Details are in Appendix B. Among diversity incentive methods, we detected no invalid utterance, in line with the findings of (Cegin et al., 2023).

heading: 4.2 Lexical diversity of paraphrases
text: Next, we investigated the effect of diversity incentive methods on the lexical diversity of the collected datasets. We focused on the number of collected unique words (vocabulary) and the number of collected unique 3-grams for each dataset. As we repeated the data collection process 5 times for each dataset and LLM combination, we report the mean numbers of collected unique words and 3-grams. We visualize our findings in Appendix I.
In nearly all cases except for one (ChatGPT for the AG News dataset) the taboo method yielded a higher-than-baseline number of unique words and 3-grams. The hints and chaining methods yielded only occasional increases in lexical diversity, with fluctuating results of increased and decreased lexical diversity across LLMs and datasets. However, the resulting increases in lexical diversity were not statistically significant, as we investigated using the Wilcoxon signed-rank test (p=0.05).
In more details, the taboo method increased mean no. unique words 30/30 cases and no. unique 3-grams 29/30 cases. The chaining method had better diversity than the baseline in 9/30 cases for unique ngrams and in 4/30 cases the diversity was similar. It achieved better diversity in 10/30 cases for unique words and similar in 9/30 cases. The hints method yielded similar results, achieving better no. of unique ngrams than the baseline 10/30 cases and similar in 5/30 cases, while achieving better no. unique words in 9/30 cases and 8/30 cases it was similar to the baseline. The relative increase in lexical diversity ranges from approx. 2% (Yelp, SST-5 datasets) to 10 % (ATIS, FB datasets) for both no. unique words and 3-grams.
In summary, even thought the taboo method increases the lexical diversity in nearly all of the cases, the increase is not statistically significant. This contrasts with the crowdsourcing literature. It indicates that the LLMs are using lexically rich vocabulary already with the base prompting, hence the low benefit of diversity incentive methods.

heading: 4.3 Ablation study results
text: Here, we compared the number of collected ngrams and words between the ablated and nonablated diversity incentive methods. We label methods as of similar performance if the difference in the number of collected 3-grams or words is less than 10 and we also perform statistical tests. We report the difference between non-ablated and ab-
lated methods in Figures 7 and 6. The non-ablated taboo method has betters results in both words (19/30 cases better, 8/30 similar, 3/30 worse) and n-grams (22/30 cases better, 7/30 similar, 1/30 worse) collected than its ablated counterpart. This indicates that the use of the most significant words helps LLMs generate more diverse data in most cases. In contrast, the non-ablated chaining and hints methods yield better diversity in only 8/30 cases for number of unique words and even less so for the number of unique 3-grams. In more than half of the cases the lexical diversity decreased. This indicates that the usage of outliers as seed sentences or as examples is not desirable when targeting higher lexical diversity.
We answer the RQ1: Does the usage of diversity incentive methods on LLMs yield more diverse paraphrases? as follows: the usage of the taboo method increases the lexical diversity of collected data when compared to both the baseline method and the ablated version of the method itself. Other two methods however affect the diversity of collected paraphrases only randomly. These changes are, however, not statistically significant, indicating that the LLMs use rich lexical vocabulary even without the diversity incentives themselves.

heading: 5 Finetuning models on data collected via diversity incentive methods
text: To investigate whether the diversity incentive methods improve the performance of downstream models, we finetuned BERT-large 5 times and Mistral 3 times for each LLM-dataset combination. Additionally, as we work with limited data, which was found to cause large variance and instability in finetuning results (Mosbach et al., 2020, 2023; Pecher et al., 2023; Chang and Jia, 2023), we sampled data 5 times. This resulted in 25 finetuned classifiers for BERT (5 data collection rounds and 5 finetunings for each of those data collection rounds) and 15 for Mistral that we evaluate per dataset-LLM combination. The full details about hyperparameters and the finetuning setup of BERT and Mistral classifier can be found in Appendices D and E respectively. We report the accuracy of the finetuned models on the test split of each dataset and focus on 2 main attributes: mean accuracy and stability of performance (by measuring standard deviation of accuracy).Additionally, we also conducted Mann-Whitney-U tests (p=0.05) between the baseline prompt method and other diversity incentive
methods. We are interested in consistent, better performance of a diversity incentive method over the prompt baseline across LLMs and datasets, as fluctuating performance could be an indicator of random effects. See summary in Table 1 and full results in the Appendix C.

heading: 5.1 Impact of diversity incentives on model performance
text: In terms of mean achieved accuracy from all diversity incentive methods while finetuning BERT, the hints method achieved best performance across all LLM and dataset combinations by consistently outperforming or achieving similar mean value as the baseline prompt method in 28 out of 30 LLM and dataset combinations - 20 cases of better mean performance and 9 cases of similar (difference less than 0.1%). However, only 3 out of the 19 (15.79%) increases were statistically significant. Finetuning of Mistral yielded stronger results as the hints method achieved better performance 25/30 times, 4/30 times the performance was similar and once worse. Out of the 25 times the hints method performed better, 14 times (56%) it was statistically significant. We speculate that this might be due to better capabilities of the model to use the augmented data. In terms of LLMs used for data augmentation, the statistically significant increases were achieved in 3/6 cases for Platypus and Mistral, in 4/6 cases for LLaMA2 and GPT-4. The relative increase in mean performance ranged from 0.6% to 2.5% better performance than the baseline for BERT and 1% to 11% for Mistral.
The taboo method did significantly worse than the baseline for BERT in 3/30 cases, and only once better. On the other hand, the decrease on Mistral happened in 2/30 cases, while 9/30 times there was a significant increases in performance. The taboo method achieved better results on Mistral, similar to the hints method. The chaining method did not perform better or worse in most cases, yielding a very similar mean performance in most cases for both BERT and Mistral.
In terms of performance stability, BERT finetuned on data collected via the hints method achieved better stability of performance (standard deviation relative difference less than 5%) in 22/30 cases and similar stability of performance in 5 cases. For Mistral, better stability was achieved in 26/30 cases, with 2 cases of similar and 2 cases of worse stability (on the FB dataset). The relative increase of stability over baseline prompt method
is from approx. 5% to 35% for BERT and from 10% to 66% for Mistral.
The taboo method achieves better stability for 14/30 cases, 9/30 cases it is worse than the baseline and 7/30 cases the stability is similar for BERT. For Mistral the results are similar: 15/30 cases the stability is better, 11/30 cases it is worse and 4/30 cases it is similar to the baseline. The chaining method achieves better stability of performance only half of the time for BERT and 18/30 cases for Mistral, with 8 cases of worse stability.
In nearly all cases the hints method achieves higher mean performance than the baseline prompt method and on average achieves higher stability of performance as seen by decreased standard deviation and increased minimum value of finetuned models for both BERT and Mistral. The increases in mean performance and stability are more significant for Mistral than BERT, being statistically significant in 14/26 cases of better performance. The taboo method more often than not increases the performance over the baseline and achieves lower stability, but only does so around half of the time, which could indicate random chance at play. Models finetuned on data collected using the taboo method can also underperform significantly. The chaining method performs similar to the taboo method, with fluctuating results in both stability and mean performance.

heading: 5.2 Ablation study results
text: Similar to the Section 4, we evaluate the diversity incentive methods also terms of an ablation study conducted via details from Section 3.4 to investigate whether the setup of the methods themselves contributes to their performance. We visualize our findings in Appendix K.
The non-ablated hints method has in 29/30 cases better mean performance that the ablated version for BERT and in 27/30 cases for Mistral, with statistically significant results in 8/30 cases for BERT and 4/30 for Mistral. This might indicate that the usage of outliers as hints for the LLMs tends to increase the quality of collected data in data augmentation scenarios when compared to hints chosen randomly as in the ablated method.
The non-ablated taboo method achieves better mean performance in 8/18 cases (all of them statistically significant) for BERT and 12/30 cases for Mistral (6 cases of statistical significance). However, the ablated version of taboo method was better than the non-ablated version in 4/30 cases signifi-
cantly. This implies that the use of most significant words as taboo instructions for the LLMs has no significant effect in data augmentation. The nonablated version of chaining method achieves better mean performance in 9/30 cases (4 cases of statistical significance) for BERT and in 12/30 for Mistral (3 cases of statistical significance). For Mistral, in equally 3 cases the results were statistically worse. This implies, similar to the taboo method, that the usage of previous outliers as seed sentences has no significant effect on LLMs in a data augmentation scenario when compared to the usage of random previous paraphrases as seed sentences.
We answer the RQ2: Do classifiers achieve better performance if trained on data augmented using diversity incentive methods on LLMs? as follows: only models finetuned on data collected via the hints method achieve better stability and mean performance than those trained on data collected via the baseline prompt method. The hints method also achieves better mean performance and stability of performance when compared to its ablated version. The data collected via the taboo and chaining methods have random influence on the performance of finetuned models. These results indicate that the usage of outliers as hints for LLMs in a data augmentation scenarios is beneficial, while other methods have no advantage over the baseline of using only prompt instructions.

heading: 6 Combining diversity incentives
text: As the taboo method achieved best results in lexical diversity in and the hints method achieved best results in model performance, as follow-up, we decided to combine these two methods to see if we can achieve an improvement. We have performed the data collection and finetuning process in the same way as described in Section 3.
In terms of lexical diversity, the method itself does not have any statistical significance on the results, although the mean number of unique words is higher than the baseline in 18/30 cases and the number of unique n-grams is higher in 16/30 cases. However, in some of the remaining cases a considerable (more than 5%) drop can be observed. In terms of model performance, the combined method statistically significantly decreased the model performance over baseline in 5/30 cases with no increases for BERT and increases performance in 4/30 cases for Mistral. Additionally, it always performed worse as either the hints or taboo method.
In summary, the combination of hints and taboo method into one method grants little to no advantage over either of the methods in both lexical diversity and model performance. We hypothesize that this might be due to the more complicated instructions to the LLM when collecting the data. A decoupling of the methods in a chain of tasks could potentially improve this approach in the future.

heading: 7 Discussion
text: Given the results of our experiments, we note these following observations: First, contrary to the performance of diversity incentive methods observed by related work in crowdsourcing settings (better lexical diversity of paraphrases and better performance of downstream models), not all of the methods show improvement of the lexical diversity when used with LLMs. The worst performing method is the chaining method, where recent works already pointed out that LLMs create progressively worse paraphrases when using their own outputs as seed sentences repeatedly (Tripto et al., 2023). However, none of the changes in lexical diversity are of statistical significance.
Second, the best performing method for data augmentation is the hints method, which is similar to in-context learning where demonstrations of samples are provided to the LLM as part of the prompt. This might be the reason why this method works so well, as the own paraphrases of the LLM guide it to better output, similar to in-context learning.
Third, we observe that, contrary to some previous works (Larson et al., 2020; Joshi and He, 2022), the lexical diversity of the paraphrases does not correlate with performance of models trained on them. Even though the data collected using the taboo method yield highest lexical diversity, models trained on such data do not achieve consistently better performance against baseline.
Fourth,the increase in mean performance and stability seems to be small, but in relative terms (compared to the baseline method) it seems to be significant, as the increase of mean performance can range from 0.6% to 2.5% increase over baseline for BERT and 1% to 11% increase for Mistral. For stability, the increases are even more significant: for BERT the range is between 5% to 35% increase over baseline and for Mistral from 10% to 66%.
Fifth, diversity incentives require additional computations (for significant words and outlier paraphrases) and also require larger LLM context (e.g.,
hints use additional paraphrases in instructions of the model), meaning higher costs. As such, the increased computation costs may not warrant the use of diversity incentives.
Sixth, the combination of the best method for lexical diversity (taboo) and best method for model performance (hints) did not yield the increases in both lexical diversity and model performance, but performed rather poorly. We hypothesize that this might be due to the increased context length for the LLM with additional instructions that are hard to perform in one single action.
The promising results using the hints method opens possibilities for investigations of incontext learning for text generation in LLMs, as the quality of such generated data using hints seems to be better than without them. This is in line with the recent results (Cox et al., 2023) that indicate that the usage of previous examples in instructions for LLMs leads to better generated data.

heading: 8 Conclusion
text: In this work, we investigated the effects of different diversity incentive methods used in crowdsourcing on the lexical diversity of LLM-augmented textual datasets and performance of classification models trained on such data. We compared 3 of such methods with a baseline of using only prompts asking the LLM to paraphrase a given seed. We experimented with 5 LLMs on 6 datasets. Our results indicate that the taboo method increases lexical diversity of the collected data, but that this change is not of statistical significance and affects performance only randomly. The hints method affects lexical diversity randomly, but increases the performance of classification models (both in stability of and mean performance) that were trained on data collected using this method. The chaining method does not improve lexical diversity or model performance of classification models trained on data collected using this method. The combination of hints method and taboo method does not significantly increase the lexical diversity or model performance. A common downside of diversity incentive methods is the increase of inference costs. Also, there is still some randomness present when using these methods, as even the best performing methods do not increase lexical diversity or performance of models in all cases.
The notable relative increase in stability of performance and mean performance of models trained
on data collected using the hints method indicates that LLMs can produce data of better quality using this method when aiming for downstream task classifier performance.
Limitations
We note several limitations to our work. First, we did not explore the usability of the diversity incentive methods for languages other than English or for multi-lingual language models.
Second, we did not use different types of prompts in our experiments and followed those used in previous studies (Cegin et al., 2023; Larson et al., 2020). Different prompts could have effects on the quality of LLMs, but would radically increase the size of this study, and as such we decided to leave this for future work.
Third, we evaluated the Mistral finetuned models on only a subset of the test data to save computational resources as some datasets had large test data sets similar to other works (Chang and Jia, 2023; Li and Qiu, 2023; Gao et al., 2021; Köksal et al., 2023). We did, however, use different splits for each finetuning to mitigate the impact of sample bias.
Fourth, the worse results of the taboo method might be due to the fact that the method sometimes uses unrelated words. Taboo words are determined per label which may yield words with little relevance to the seed sentence. This limitations stems from our replication of the method from crowdsouring, where no such filters were described in the original work of (Larson et al., 2020).
Fifth, we have collected data from only 2 opensource LLMs as well as from LLaMA-2, but we believe that the inclusion of different LLMs and the consistent improvement of hints method on model performance across data collected from various LLMs does not threaten our findings.
Sixth, we only used 2 classifiers for finetuning, namely BERT-large and Mistral-7B-v0.1. However, we believe that repeated data collection rounds and multiple finetunings on the collected data for a variety of datasets mitigates this drawback and as such it does not threaten our findings.
Seventh, we collected 5 samples per seed sentence and used 6 seed sentences per label from the datasets we used, which resulted in (relatively to the original datasets) smaller datasets (ranging from approx. 200 to 700 sampels). The total amount of all collected paraphrases amounts
to 253,500 paraphrases, reflecting the multiple data collection rounds datasets and LLMs we used. However, we did not investigate the effects of diversity incentives on larger amounts of collected data to investigate if such data augmentation methods decrease in effectiveness when augmenting larger amounts of seed data.
Eight, we only used the default settings of the diversity incentive methods and thus we did not compare the different number of seed sentences other than 6 per label and different number of hints to investigate the effectiveness of the diversity incentive methods under different settings.
Ninth, the reproducibility of our data collection process for ChatGPT and GPT-4 is dependent upon the owners of ChatGPT services as the models we used in our study might be deprecated and not available in due time. This is, however, counterbalanced by the inclusion of 3 open LLMs.
Tenth, we do not know if any of the 6 datasets used in this study have been used for training the LLMs we used for data collection and if this had any effect on our results and findings. As such, we do not know what kind of effect the diversity incentive methods would have on data augmentation of new, unpublished datasets. This limitation is part of the recently recognized possible “LLM validation crisis”, as described by (Li and Flanigan, 2023).
Eleventh, we do not provide a direct comparison of LLMs against each other, as the seed sentences used for each data collection round changed for each LLM randomly. We believe, however, that this does not threaten the results, as the goal of this paper is to compare diversity incentive methods in a direct comparison on LLMs, not LLMs between each other.

heading: Acknowledgements
text: This work was partially supported by AI-CODE (GA No. 101135437), VIGILANT (GA No. 101073921), and vera.ai (GA No. 101070093), projects funded by the European Union under the Horizon Europe. This work was also partially supported by MODERMED a project funded by the Slovak Research and Development Agency, GA No. APVV-22-0414.
Part of the research results were obtained using the computational resources procured in the national project National competence centre for high performance computing (project code:
311070AKF2) funded by European Regional Development Fund, EU Structural Funds Informatization of society, Operational Program Integrated Infrastructure. This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254).

heading: A Ethical considerations
text: Based on a thorough ethical assessment, performed on the basis of intra-institutional ethical guidelines and checklists tailored to the use of data and algorithms, we see no ethical concerns pertaining directly to the conduct of this research. We also ethically assessed our paraphrase validity crowdsourcing process from Appendix B via our intrainstitutional ethical guidelines and found no ethical concerns. In our study, we analyzed existing data or data generated using various LLMs. During our manual checking of the data in Section 3 we also ensured that the data contained no personal or offensive data. Albeit production of new data through LLMs bears several risks, such as introduction of biases, the small size of the produced dataset, sufficient for experimentation is, at the same time, insufficient for any major machine learning endeavors, where such biases could be transferred.
We follow the license terms for all the models and datasets we used (such as the one required for the use of the LLaMA-2 model) – all models and datasets allow their use as part of research.
A.1 CO2 Emission Related to Experiments
Data collection via open-source LLMs was conducted using a private infrastructure, which has a carbon efficiency of 0.432 kg CO2/kWh. A cumulative of 100 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W) for data collection.
Model finetuning for both BERT and Mistral was conducted using a private infrastructure, which has a carbon efficiency of 0.432 kg CO2/kWh. A cumulative of 800 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W) for data collection.
Total emissions together are estimated to be 97.2 kgCO2 of which 0 percents were directly offset. We tried to reduce the generated emissions by using 4-bit quantization for LLMs and using a subset of test data for evaluation for Mistral finetuning, as inference is costly.
Estimations were conducted using the MachineLearning Impact calculator presented in (Lacoste et al., 2019).

heading: B Paraphrase validity checking process
text: For the process of checking the validity of the created paraphrases, we used our very own web app developed for this process. The users, who were the authors that also developed the app, were shown the seed samples and its label, from which LLM generated the paraphrases, and one particular paraphrase to validate. The authors/users all gave consent to the data collection process and had knowledge of how the data would be used. The instructions were "Please decide if the paraphrase has the same meaning as the seed sentence and if it adheres to the label of the seed sentence." The user 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 re-visions. As the seed sentence changed only once in a while (we first showed all the paraphrases from one seed sentence) this significantly reduced the cognitive load on the annotator. The users/authors then discussed together the ‘borderline cases’ where the users were not sure about the validity of created paraphrases.

heading: C Full results of model performance on
text: In this section we report full result of our experiments for each dataset, diversity incentive method
and LLM. The results for BERT-large are in Table 2 and for Mistral in Table 3. Visualizations can be found in Appendix J. The specific open-source LLMs used for data collection were LLaMA-270B-instruct 2 with 70 bilion parameters, Mistral-
2https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
7B-instruct 3 with 7 bilion parameters and Platypus70B-instruct 4 with 70 bilion parameters.
3https://huggingface.co/mistralai/Mistral-7B-Instructv0.1
4https://huggingface.co/garage-bAInd/Platypus2-70Binstruct

heading: D BERT-large finetuning details
text: We used the bert-large-uncased version of the model from Huggingface and the best working hyperparameters from our hyperparameter search were batch size of 32, classifier dropout set to 0.2,
used the AdamW optimizer with learning rate set to 1e-5 and trained for 80 epochs. We evaluated the model during training after each 10 epochs and saved its performance. We reported the best test performance for each of the models during training.

heading: E Mistral finetuning details
text: We used the Mistral-7B-v0.1 5 version of the model from Huggingface. For finetuning, we used the PEFT method QLoRA (Dettmers et al., 2023) in 4-bit setting with r=16 and alpha=16. We finetuned the model for 20 epochs, used batch size of 32, learning rate of 2e-5, dropout of 0.1, used halfprecision floating-point format (fp16), warmup ratio of 0.1, maximum grad. norm of 0.3, maximum sequence length of 128, weight decay set to 0.01 and used 8-bit Adam optimization. We evaluated the model on a subset of the test dataset (10% of the original dataset) due to the lengthy inference times, similar to previous work (Chang and Jia, 2023; Li and Qiu, 2023; Gao et al., 2021; Köksal et al., 2023) on all of the datasets except for ATIS. We did, however, use different splits from the test part of the datasets to mitigate the effect of sample bias.

heading: F Dataset details
text: As we did not use all of the dataset labels and samples in each of the dataset, we list our setup here. We mostly used labels that were in the datasets with similar quantity to deal with the imbalanced datasets issue. All used datasets are in English language. For the 20 News dataset we used samples with labels politics, wellness, entertainment, travel, style and beauty and parenting. For the AG News, SST-5 and Yelp datasets we used all the samples. For the ATIS dataset we used samples with labels atis_abbreviation, atis_aircraft, atis_airfare, atis_airline, atis_flight, atis_flight_time, atis_ground_service and atis_quantity. For the FB dataset we used samples with labels get_directions, get_distance, get_estimated_arrival, get_estimated_departure, get_estimated_duration, get_info_road_condition, get_info_route, get_info_traffic, get_location and update_directions.

heading: G Number of collected samples per dataset
text: For the 20 News datasets we used 36 seed samples (6 seed per label with 6 labels total) randomly sampled for each data collection round, resulting in 180 samples collected for each round and 396 samples in the final dataset (48 seed samples + 180 samples 1st round + 180 samples 2nd round). The entire
5https://huggingface.co/mistralai/Mistral-7B-v0.1
test split we used for BERT finetuning had 11,751 samples.
For the AG News dataset we used 24 seed samples randomly sampled for each data collection round, resulting in 120 samples collected for each round and 264 samples in the final dataset. The entire test split we used for BERT finetuning had 7,600 samples.
For the ATIS dataset we used 48 seed samples randomly sampled for each data collection round, resulting in 240 samples collected for each round and 528 samples in the final dataset. The entire test split we used for BERT finetuning had 763 samples.
For the FB dataset we used 60 seed samples randomly sampled for each data collection round, resulting in 300 samples collected for each round and 660 samples in the final dataset. The entire test split we used for BERT finetuning had 5,645 samples.
For the SST-5 dataset we used 30 seed samples randomly sampled for each data collection round, resulting in 150 samples collected for each round and 330 samples in the final dataset. The entire test split we used for BERT finetuning had 2,210 samples.
For the Yelp dataset we used 30 seed samples randomly sampled for each data collection round, resulting in 150 samples collected for each round and 330 samples in the final dataset. The entire test split we used for BERT finetuning had 13,895 samples.

heading: H Templates and examples of diversity incentive prompts used in LLMs
text: The prompt and chaining method: Rephrase an original question or statement 3 times. Original phrase: seed_phrase.
Prompt example
Rephrase an o r i g i n a l q u e s t i o n o r s t a t e m e n t 3 t i m e s . O r i g i n a l p h r a s e : " t e l l me t h e f a s t e s t way t o g e t home " .
C h a i n i n g example
Rephrase an o r i g i n a l q u e s t i o n o r s t a t e m e n t 3 t i m e s . O r i g i n a l p h r a s e : " p l e a s e s h a r e t h e most
r a p i d means o f g e t t i n g back t o my d w e l l i n g " .
The taboo method: Rephrase an original question or statement 3 times. Original phrase: seed_phrase. Don’t use the words “word_1”,
“word_2" or “word_3” in your responses.
Taboo example
Rephrase an o r i g i n a l q u e s t i o n o r s t a t e m e n t 3 t i m e s . O r i g i n a l p h r a s e : " t e l l me t h e f a s t e s t way t o g e t home " .
Don ’ t use t h e words " a r r i v e " , " c o n s t r u c t i o n " o r " house " i n your r e s p o n s e s .
The hints method: Rephrase an original question or statement 3 times. Original phrase: seed_phrase. ### Example paraphrases: phrase_1, phrase_2, phrase_3 ###
H i n t s example
Rephrase an o r i g i n a l q u e s t i o n o r s t a t e m e n t 3 t i m e s . O r i g i n a l p h r a s e : " t e l l me t h e f a s t e s t way t o g e t home " .
### Example p a r a p h r a s e s : " p l e a s e s h a r e t h e most r a p i d
means o f g e t t i n g back t o my d w e l l i n g " .
" i n f o r m me of t h e q u i c k e s t r o u t e t o r e a c h my house " . " what i s t h e s w i f t e s t method t o a r r i v e a t my r e s i d e n c e " . ###
I Visualization of the effect of diversity incentive methods on lexical diversity
The effects of diversity incentive methods on lexical diversity can be found in Figure 2 for no. unique 3-grams and Figure 3 for no. unique words.
J Visualization of the effect of diversity incentive methods model performance
The effects of diversity incentive methods on model performance can be found in Figure 4 for BERTlarge and Figure 5 for Mistral-7B.

heading: K Results of the ablation study
text: In this section we list visualizations of the results for ablated versions of different diversity incentive methods in lexical diversity in Figures 6 and 7 and in accuracy of models trained on data collected this way in Figures 8 and 9.

heading: Future Work
text: Different prompts could have effects on the quality of LLMs, but would radically increase the size of this study, and as such we decided to leave this for future work. Third, we evaluated the Mistral finetuned models on only a subset of the test data to save computational resources as some datasets had large test data sets similar to other works (Chang and Jia, 2023; Li and Qiu, 2023; Gao et al. , 2021; Köksal et al. , 2023). We did, however, use different splits for each finetuning to mitigate the impact of sample bias. Fourth, the worse results of the taboo method might be due to the fact that the method sometimes uses unrelated words. Taboo words are determined per label which may yield words with little relevance to the seed sentence. This limitations stems from our replication of the method from crowdsouring, where no such filters were described in the original work of (Larson et al. , 2020). Fifth, we have collected data from only 2 opensource LLMs as well as from LLaMA-2, but we believe that the inclusion of different LLMs and the consistent improvement of hints method on model performance across data collected from various LLMs does not threaten our findings. Sixth, we only used 2 classifiers for finetuning, namely BERT-large and Mistral-7B-v0. 1. However, we believe that repeated data collection rounds and multiple finetunings on the collected data for a variety of datasets mitigates this drawback and as such it does not threaten our findings. Seventh, we collected 5 samples per seed sentence and used 6 seed sentences per label from the datasets we used, which resulted in (relatively to the original datasets) smaller datasets (ranging from approx. 200 to 700 sampels). The total amount of all collected paraphrases amounts
to 253,500 paraphrases, reflecting the multiple data collection rounds datasets and LLMs we used. However, we did not investigate the effects of diversity incentives on larger amounts of collected data to investigate if such data augmentation methods decrease in effectiveness when augmenting larger amounts of seed data. Eight, we only used the default settings of the diversity incentive methods and thus we did not compare the different number of seed sentences other than 6 per label and different number of hints to investigate the effectiveness of the diversity incentive methods under different settings. Ninth, the reproducibility of our data collection process for ChatGPT and GPT-4 is dependent upon the owners of ChatGPT services as the models we used in our study might be deprecated and not available in due time. This is, however, counterbalanced by the inclusion of 3 open LLMs. Tenth, we do not know if any of the 6 datasets used in this study have been used for training the LLMs we used for data collection and if this had any effect on our results and findings. As such, we do not know what kind of effect the diversity incentive methods would have on data augmentation of new, unpublished datasets. This limitation is part of the recently recognized possible “LLM validation crisis”, as described by (Li and Flanigan, 2023). Eleventh, we do not provide a direct comparison of LLMs against each other, as the seed sentences used for each data collection round changed for each LLM randomly. We believe, however, that this does not threaten the results, as the goal of this paper is to compare diversity incentive methods in a direct comparison on LLMs, not LLMs between each other