Human Learning by Model Feedback: The Dynamics of Iterative Prompting with Midjourney
Authors: Shachar Don-Yehiya
Leshem Choshen
Omri Abend
Venue: EMNLP 2023 Main (2023)
Keywords: Interaction
Text-to-Image
alignment
Cognitive Science

Abstract: Generating images with a Text-to-Image model often requires multiple trials, where human users iteratively update their prompt based on feedback, namely the output image. Taking inspiration from cognitive work on reference games and dialogue alignment, this paper analyzes the dynamics of the user prompts along such iterations. We compile a dataset of iterative interactions of human users with Midjourney. Our analysis then reveals that prompts predictably converge toward specific traits along these iterations.
We further study whether this convergence is due to human users, realizing they missed important details, or due to adaptation to the model's ``preferences'', producing better images for a specific language style. We show initial evidence that both possibilities are at play.
The possibility that users adapt to the model's preference raises concerns about reusing user data for further training. The prompts may be biased towards the preferences of a specific model, rather than align with human intentions and natural manner of expression.

Human Learning by Model Feedback:
The Dynamies of Iterative Prompting with Midjourney
Shachar Don-Yehiya Leshem Choshen Omri Abend
'The Hebrew University of Jerusalem, MIT (first.last lemal.. ac. il
Abstract
Generating images with a Text-to-Image model often requires multiple trials, where human users iteratively update their prompt based on feedback, namely the output image. Tak- ing inspiration from cognitive work refer- ence games and dialogue alignment, this pa- per analyzes the dynamics of the user prompts along such iterations. We compile dataset of iterative interactions of human users with Midjourney.' 1 Our analysis then reveals that prompts predictably converge toward specific traits along these iterations. We further study whether this convergence due human users, realizing they missed important details, or due to adaptation to the model's "preferences" pro- ducing better images for a specific language style. We show initial evidence that both pos- sibilities are at play. The possibility that users adapt the model's preference raises concerns about reusing user data for further training. The prompts may be biased towards the preferences of specific model, rather than align with hu- man intentions and natural manner of expres- sion.
handsome boy with hair black and hazel eyes
young man 18 age with short brown hair and hazel eyes and types with laptop
mini university dinosaur with churos glasses
cute college dinosaur curly glasses
Figure 1: Threads examples. The user adjusts their prompts along the interaction. The first thread gets more concrete ("young man 18 age" instead of "boy") and longer ("types with laptop"). The second changes wording ("cute" instead of "mini", "college" instead of "university").
1 Introduction
Text-to-image models have become one of the most remarkable applications in the intersection computer vision and natural language process- ing (Zhang et al., 2023). Their promise, to generate an image based on natural language description, is challenging not only to the models, but to the hu- man users well. Generating images with the de- sired details requires proper textual prompts, which often take multiple turns, with the human user up- dating their prompt slightly based on the last image they received. We see each such interaction as a "thread", a sequence of prompts, and analyze the dynamics of user prompts along the interaction. We are not aware of any work that examines the dynamics of the prompts between iterations.
To study this question, we compile the Mid- joureny dataset, scraped from the Midjoureny Dis- cord server?, containing prompts and their corre- sponding generated images and metadata, orga- nized as 107, 051 interaction threads.
The language people use when they interact with each other changes over the course of the conver- sation (Delancy-Buse) et al., 2019). Thcoretical work suggests that learning mechanisms may allow interlocutors to dynamically adapt not only their vocabulary but their representations of meaning (Brennan and Clark, 1996; Pickering and Garrod, 2004, 2021). We hypothesize that also when in-
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Code and data are available in: https://githun com/

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teracting with Midjourney, where only the human user is able adapt and the Midjourney model remains "frozen", we would see a systematic lan- guage change along the iterations.
the true target image. Empirical findings show a number of recurring behavioral trends in the ref- erence game task. For example, descriptions are dramatically shortened across iterations (Krauss and Weinheimer, 1964), and the resulting labels are partner-specific (Wilkes- (iikees-ibbs and Clark, 1992; Brennan and Hanna, 2009).
Unlike the interaction with general assistant models (Köpf et al., 2023), which might include multiple topies and change along the conversation, the interaction thread of text-to-image model con- tains attempts to generate one image, a single scene, with no major content change. This allows us to better recognize the change in the linguistic fea- tures rather than the content.
We view the interaction thread as similar to a repeated reference game of the human user with the model. The human user directs Midjourney with textual prompts to generate (instead of select) the target image. Unlike the original game, only the human user is changing along the interaction based on the feedback (i.e., the image) they get from Midjourney. The Midjourney model is 'frozen', not able adjust the user feedback.
Our analysis reveals convergence patterns along the threads, i.e., during interactions humans adjust shared features that bring them closer to their ideal image in terms of prompt length, perplexity, concreteness and more. Still, it is unclear whether these adjustments are due to humans adding miss- ing details, or due to matching the model prefer- ences generating better images due to prompts in a language style that is casier for it to infer, thus encouraging users to adapt to it. We find evidence for both.
We hypothesize that also in our semi-reference game where only the human user is able to adapt we would see language change along the iterations. We use similar methods to those used in recent works (Ji et al., 2022; Hawkins et al., 2019), in order to examine this change.
Prompt and image pairs, together with their meta- data about upscale requests, can be seen as a great source of data for Reinforcement Learning from Human Feedback (RLHF). In RLHF, non-expert human preferences are used to train the model to achieve better alignment (Christiano et al., 2017; Bai et al., 2022; Lee et al., 2023; Wu et al., 2023)- We discuss in $9 the possible consequences of reusing the Midjourney data.
The second possibility, that users adapt to the model preferences, calls for caution regarding the subsequent use of human data from human-model interaction. For example, we could take the "suc- cessful" images that the human users presumably liked and requested a high resolution version of them ("upscale"), and use them with their match- ing prompts as a free human-feedbadd dataset (Bai et al., 2022). However, given that these prompts may be biased towards the model's preferences, training on them would create a model that has even more model-like' behaviour.
Data
In this section, we describe the choices and process of acquiring text-to-image interactions. We start by discussing the reasons to pick Midjourney data rather than other text-to-image data sources.
We hope that by releasing this first iterative prompting dataset, along with our findings regard- ing possible biases in the human data, we would encourage more work on human-model alignment and interaction.
One reason to prefer Midjourney over open- source text-to-image models is its strong capabili- ties. Midjoumny can handle complicated prompts, making the human-model interaction closer to a standard human interaction.
2 Background
In a repeated reference game (Clark and Wilkes- Gibbs, 1986), pairs of players are presented with a set of images. On each iteration, one player (the director) is privately shown which one is the target image. and should produce a referring expression to help their partner (the matcher) to correctly select that image. At the end of the iteration, the director is given feedback about which image the matcher selected, and the matcher is given feedback about
Another reason to prefer Midjourney is the avail- ability of the prompts, images and meta-data on the Discord server. We construct the dataset by scrap- ing user-generated prompts from the Midjourncy Discord server. The server contains channels in which a user can type a prompt and arguments, and then the Midjourney bot would reply with gener- ated images, combined together into a grid. Then, the user is satisfied with one of the 4 images, they

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can send an "upscale" command to the bot, to get an upscaled version of the desired image.
was upscaled, we assume is of good quality and matches the user's intentions.
We randomly choose one the "newbies" chan- nels, where both new and experienced users are experimenting with general domain prompts (in contrast to the "characters" channel for example). We collect 693, 528 prompts (From 23 January to March 2023), together with their matching im- ages and meta-data such as timestamps and user ids (which we anonymize).
Although this a reasonable assumption, this is not always the case. A user can upscale an image because they think the image is so bad that it is funny, or if they want to record the creation process. We expect it, however, to be of a small effect on the general "upscale" distribution.
Out of all the prompts, 25% were upscaled.
Splitting into Threads
In $F we repeat some of the experiments with data from Stable Diffusion (Rombach et al., 2021), I), concluding that our results can be extended to other models.
We split the prompts into threads. Each thread should contain a user's trails to create one target image. However, it is often difficult to determine whether the user had the same image in mind when they tried two consecutive prompts. For example, when user asks for an image of "kids playing at the school yard" and then replaces "kids" with "a kid", it is hard to tell whether they moved to describe a new scene or only tried to change the composition. We consider a prompt to belong to a new thread according to the following guidelines:
3.1 Data Cleaning
The Midjoumey bot is capable of inferring not only textual prompts, but also reference images. Since we are interested in the linguistic side, we filter out prompts that contain images. We also limit ourselves to prompts in the English language, to allow a cleaner analysis. We remove prompts with no text, or no matching generated image (due to technical problems). After cleaning, we remain with 169, 620 prompts.
1. Even when ignoring the subtle details, the current prompt describes a whole different scene than the previous one. It excludes cases where the user changed large element in the scene, but the overall intention of the scene was not altered.
The Midjourney bot can get as part of the input prompt some predefined parameters like the aspect ratio, chaos and more.* 4 provided at the end of the prompt. We separate these parameters from the rest of the text, so in our analysis we will be looking at natural language sentences. In $C we repeat some of the experiments with prompts with the default parameters only (i.e., with no predefined parameters).
2. The main subjects described in the current prompt are intrinsically different from the sub- jects in the previous one. For example, an intrinsic change would be in the previous prompt the main character was a cat, and in the current it is dinosaur. If kid is changed into kids, or boy is changed into girl, it is not. An expectation is when a non-intrinsic change seems to change the whole mcaning of the scene.
3.2
Data Statisties
The dataset contains prompts from 30, 394 differ- ent users, each has 5.58 prompts average with standard deviation 20.52. 22, 563 users have more than one prompt, and 4008 of them have more than 10 each.
3. The current prompt can not be seen as up- dated version of the previous prompt.
3.3 Upscale
As mentioned, when a user is satisfied with one of the grid images, they can send an upscale com- mand to obtain upscaled version of it. We collect these commands, tion of the users from the images. If image
More examples with explanations are provided in SA.
4.1 Automatic Thread Splits
estimation the satisfac-
We propose methods to split the prompts into threads. 7, 831 of the users have one prompt only, so we mark each of them as an independent thread. To handle the remaining prompts, we use the fol- lowing methods:
*We use spacy language detector https://spacy.. io/ mniverse/projjet/ppcyy.. "See full list here: https://docs. wwddoourney.com https://dacs.mmddoorreeyyce docs/parameter-list
com/

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With our automatic annotation method we get 107, 051 threads. The average length of thread is 1.58 prompts, with std 1.54. See Figure 2. Each user produced 3.52 different threads average with std 12.67. The longest thread is of length 77. Intersection over Union. For each pair of con- secutive prompts, we compute the ratio between their intersection and union. If one sentence is sub-sentence of the other sentence, or the intersec- tion over union is larger than 0.3, we consider the sentences to be in the same thread. Otherwise, we set the second prompt to be the first prompt of new thread.
10"
Threads (log soak)
10"
10"
BERTScore. For each pair of consecutive prompts, we compute the BERTScore similarity (Zhang et al., 2019). If the BERTSccrr is larger than threshold of 0.9, we put the sentences in the same thread.
Thread Length
We note that both methods assume non- overlapping threads and not handle interleaved threads where the user tries to create two (or more) different scenes simultaneously.
Figure 2: Histogram of the threads' lengths. There are total of 107, 051 threads (annotated automatically), 645 (.6%) them contain prompts more.
4.2 Human Annotation Evaluation
Method
We annotate prompts to assess the validity of the automatic thread splitting methods. We sampled users with at least prompts and annotated their prompts. In this way, we increase the probability of annotating longer threads. We use the principles from $4 to manually annotate the prompts. One of the paper's ' authors annotated 500 prompts, and two more authors re-annotated 70 overlapping prompts each to assess inter-annotator agreement. While annotating the prompts, we found only 7 cases of interleaved threads ($4.1). We convert them to separate threads to allow the use of metrics for quality of linear text segmentation.
Our end goal is to examine the evolution of the prompts through the interaction. We start, how- ever, by asking simpler question, namely whether there is a difference between the upscale and non- upscale prompts. Such a difference would indicate that there are predictable characteristics (however intricate) of prompt that render it better for Mid- journey, and therefore provide motivation for the human users adapt their prompts towards it. To highlight differences between the upscaled and non-upscaled, we compile a list of linguistic features, that are potentially relevant to the up- scale decision. We find several features that are statistically different between the upscaled and non- upscaled populations, and then use those features to test the evolution of the threads.
The agreement level between the three annota- tors high (0.815), measured by Fleiss' kappa. Comparing the intersection over union annotations to the 500 manual annotations, we get an Fl score 0.87 and average WindowDiff (Pevzner and Hearst, 2002) of 0.24 (the lower the better). For the BERTScore annotations, we get Fl of 0.84 and average WindowDiff 0.30. Finding the intersection over union to be better, we select it to create the threads that use for the rest of the paper.
We stress that do not argue that these features account for large proportion of the variance be- tween users. Indeed, people use Midjoumnyy for a wide range of tasks, with different levels of experi- ence, hence their prompts and preferences vary a lot. Instead, we wish to make principle point that there is systematic convergence along the threads, and that it has practical implications. Future work will control for the intentions of the user, in which expect the convergence account for larger proportion of the variance.
4.3 Threads Statistics
5.1 Image and Text Classifiers
There are evidence that predicting whether the user was satisfied with the resulting image is possible given the prompt and image (Hessel et al., 2021;
The average number of prompts that were up- scaled for each thread is 0.4 with std 0.82.

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Kirstain et al., 2023).
We identify 175 words that are probable in our datasct but not in general (see App. E).
We hypothesize that the generated image alone would still allow a good guess, looking at the gen- eral quality of the image. More surprising would be to predict the upscale decision of the user based on the prompt alone. For that, there has to be special language style or content type that leads to good images.
For each prompt, we count the number of magic words in it, and normalize it by the prompt length to obtain the magic words ratio #magic words #words
We compute the perplexity GPT-2 (Radford et al., 2019) assigns to each prompt. We use the code from the Huggingface guide. A prompt with lower perplexity is a prompt that the model found be more likely.
Perplexity.
We formalize it as partial input problem (Guru- rangan et al., 2018; Feng et al., 2019; Don- Yenyya et al., 2022) - predicting whether a prompt and image pair was upscaled or not, based on the image alone, the prompt alone. We do not expect high performance, this is both partial input and noisy.
We use the concreteness ratings from Brysbaert et al. (2013) to assign each word with a concreteness score ranging from 1 (abstract) to 5 (concrete). We average the scores of all the words in the prompt get a prompt-level score.
Concreteness.
We split the datasct to train and test sets (80/20), and sample from both an equal number of upscaled and non-upscaled prompts to balance the data. We finetune both a Resnet18 8 (He et al., 2015) and a GPT-2 (Radford et al., 2019) with classification head on top of it (see $B for the full training de- tails). The input to the model is an image or prompt respectively, and the output is compared to the gold opscaled/non-upscalee notion.
Repeated Words. For each prompt, we count the occurrences of each word that appears more than once in the prompt, excluding stop words. We then normalize it by the length of the prompt.
Sentence Rate. We split each prompt to its com- ponent sentences according to the spacy parser. We divide the number of words in the prompt by the number of sentences, to get the mean number of words per sentence. The classification model acts as a black box, with- holding the features it uses for the classification. We hence compile list of linguistic features that may be relevant to the upscale decision (Guo al., 2023). For each of the features, we use the Whinney U test (Nachar, 2008) to examine whether the upscaled and non-upscaled prompts are from the same distribution or not. In App. D we apply this method also to the captions of the gener- ated images, to examine the semantic properties of the generated images.
5.2 Linguistic Features Analysis
Syntactic Tree Depth. We extract a constituency parse tree of the prompts with the Berkeley Neu- ral Parser (Kitaev and Klein, 2018). We take the depth of the tree as an indication of the syntactic complexity of the sentence.
5.3 Analysis of Thread Dynamics
In the previous sections ($5.1, $5.2), we exam- ined the end-point namely whether the prompt was upscaled or not. In this section, we characterize the dynamics of the prompts along the thread, to identify the learning process undertaken by human users. For each feature that changes between the upscaled and non-upscaled prompts ($5.2), we are looking to see whether these features have a clear trend along the interaction, i.e., whether they are ap- proximately monotonous. We plot the feature's av- erage value as a function of the index of the prompt in the thread. We filter threads with less than 10 prompts, so the number of prompts averaged at each index (from to 10) remains fixed. 5 This
Prompt Length. We compare the length of the prompts in words.
Magic Words. We use the term "magic words" to describe words that do not add any real content to the prompt, but are commonly used by practi- tioners. For example, words like "beautiful", "SK" and "highly detailed." They all appear more than 1000 times in the dataset, but it is not clear what additional information they add to the scene they describe. Their popularity is due the online com- munity, claiming that the aesthetics and attractive- ness of images can be improved by adding cer- tain keywords and key phrases to the textual input prompts (Oppenlaender, 2022).
'Without this restriction, we are at risk of getting mixed signals. For example, if we see that the first prompts are shorter, we can not tell whether this is because the shorter threads contain shorter prompts or hecause the threads are getting longer along the interaction.

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Length
Magic
Perplexity
Concreteness
Repeated
Sent Rate
Depth
Upscaled
16.67
0.109
2173
3.2628
0.040
14.19
6.19
Not
14.78
0.096
2855
3.2629
0.035
12.63
6.00
value
1.2e 281
8.6e
80
5.3e
0.123
3.4e 58
2.4e 101
1.8e 49
Table The linguistic features values for upscaled non- non-upscaled prompts, with their matching p value. All the features excluding the concreteness found to be significant, Magic words ratio, perplexity and repeated words ratio may indicate more 'model like" language, while the length, depth and sentence rate may indicate more details.
leaves 645 threads. In $G we present results for longer iterations (20), without filtering the shorter threads.
(p < 0.0007), indicating that they correlate with the decision upscale.
6.3 Thread Dynamics
6 Results
To examine the dynamics of the threads, we plot in Figure 3 the significant features as a function of the index of the prompt. We see clear trends: the features are approximately monotonous, with some hallucinations. The length, magic words ratio, repeated words ratio and the sentence rate go up, and the perplexity down.
In the following sections, we apply the method described in $5 to test the distribution and the dy- namics of the prompts and threads. We start by identifying that there is difference between the upscaled prompts and the rest ($6.1). We then as- sociate with specific linguistic features ($6.2). Finally, we examine not only the final decision, but the dynamics of the full interaction ($6.3).
The magic words ratio has more hallucinations than the others, with a drop the beginning. Also, unlike the other features, it does not seem to arrive to saturation within the 10 prompts window.
6.1 Upscaled and Non-Upscaled are Different
These results suggest that the users are not just randomly trying different prompt variations until they chance upon good ones. Instead the dynamics is guided in certain direction by the feedback from the model. Users then seem adapt the model, without necessarily noticing.
We train and test the image classifier on the gen- crated images as described in $5.1. We get an accuracy of 55.6% with std 0.21 over 3 seeds. This is 5.6 points above random as our data is balanced.
We do the same with the text classifier, training and testing it on the prompts. We get accuracy of 58.2% with std 0.26 over seeds. This is 8.2 points above random.
Driving Forces Behind the Dynamics
We examine two possible non-contradictory planations to the characteristics of the upsealed and non-upscaled prompts, and the direction in which the human users go along the interaction. We show supporting evidence for both.
Although this accuracy is not good enough for practical use cases, it is meaningful. Despite the noisy data and the individual intentions and prefer- ences of the users (that we do not account for), is possible to distinguish upscaled from non-upscaled images/prompts least some extent.
Option #1: Adding Omitted Details. When users input prompt Midjourney and receive image, they may realize that their original prompt lacked some important details or did not express them well enough. Writing a description for a drawing is not task most people are accustomed to. Hence, it makes sense that users learn how to make their descriptions more accurate and com- plete along the interaction.
We conclude that both the prompts and the gener- ated images are indicative to the upscale question.
6.2 Significant Features
the previous section we found that the upscaled prompts can be separated from the non upsuuleddedd prompts to some extent. Here, we study what spe- cific linguistic features correlate with this distinc- tion, to be able to explain the difference.
Results from three fcatures support this expla- nation. The prompt length, the sentence rate and the syntactic tree depth, all of them increase the interaction progresses. Improving the accuracy of a prompt can be done by adding more words to describe the details, thus extending the prompt
Table shows the mean values of the upscalle/noo--usscaledd prompts and the p values associated with the Mhnn-Wh test. All the features except the concreteness score were found to be significant after Bonferroni correction

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0,162
Magic Words
0.150
Perploaly
21.0
0.148
20.5
20.0
0.145
800
Prompt
Prompt
Prompt Length
(b) Magic Words Ratio
(c) Perplexity
0.050
0.068
0.064
Depth
8.55
0.002
18.0
6.50
Words
17.5
8.45
0.058
5.40
3
10
3
6 The /th Prompt
9 10
The 1hh Prompt
The 1th Prompt
(d) Repeated Words Ratio
(e) Sentence Rate
(f) Depth
Figure 3: Average value for each of the significant linguistic features (y-axis), as function of the prompt index (X-axis). The features are approximately monotonous, with some hallucinations. Most of the features go up (length, magic words ratio, repeated words ratio, sentence rate and tree depth) and the perplexity down. The randomly trying different prompts until they reach good ones by chance, they are guided in certain direction.
not
length. The new details can also increase the over- all complexity as reflected in the number of words per sentence and the depth the tree.
guage model assigns to the text. The perplexity of the upscaled prompts is lower than the perplexity of the non-upscaled ones, and so the perplexity of the 10th prompts compared to the first prompts. The users adapt to prompts that the model finds to be more likely. We note that it is possible to associate the descent in perplexity with the rise in length. While not logical necessity, it is common that longer texts have lower perplexity (Lu et al., 2022).
Another relevant feature the concreteness score, as one can turn a sentence to be more ac- curate by changing the existing words to more con- crete ones rather than adding new ones. Our results, however, show that the difference between the con- creteness scores is not statistically significant.
Option #2: Adopting Model-Like Language. Another possible explanation is that human users learn to write their prompt in language that is easier for the Midjourney model to handle. Human users try maximize good images by adapting to "the language of the model".
Another feature that indicates human adaptation is the repeated words ratio. Repeated words ally do add information the content, and therefore using them is not efficient. Our results, however, indicate that human users do it more of- ten as the interaction with the model progresses. It is possible that they are used to simplify ideas for the model, or to push it to give more attention to certain details.
Results from several features support this expla- nation. The magic words ratio is one of them. mentioned in $5.2, magic words do not add new content to the prompt, and from an information- theoretic standpoint are therefore mostly redundant. Yet, there are more magic words in the prompts as the interaction progresses (even when correcting for the prompt's increasing length), suggesting that this is a preference of the model that the human users adapt to.
Convergence Patterns
In the previous section, we provided two explana- tions for the observed adaptation process. this section, we further investigate the direction and destination of the adaptation.
Another such feature is perplexity. The lower the perplexity the higher the probability the lan-
For each feature f we split the threads into two

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as RLHF data, sampling from each thread one im- age that was upscaled and one that was not, coupled with the upscaled prompt. However, our findings that the human users likely adapt to the model call for caution, as we may inadvertently push models by uncritically using user data to prefer the adapted prompts even more.
geling longer
shorter
Promest Langth
24
22
20
We hope draw attention to the linguistic adap- tation process human users go through when they interact with a model. Future work will empiri- cally examine the effect of training with such data, and will expand the discussion on interactions with general language models.
The 1th Prompt
Figure The threads that get longer start relatively short, and the threads that get shorter start relatively long. Both thread groups converge towards the same length range.
10 Related Work
Text-to-image prompt engineering was studied by handful of works. Oppenlaender (2022) iden- tified prompt patterns used by the community, Pavlichenko and Ustalov (2022) examined the ef- feet of specific keywords, and Lovering and Pavlick (2023) studied at the effect of subject-verb-object frequencies on the generated image.
sets by their first and last values:
S = [hread] [(thread(O)) < [(thread(-1)
S2 = (hreadd f(thread(0)) 2 f(thread(-1)
For example, for the length feature we have one set with threads that get longer, i.e., their last prompt is longer than their first prompt. In the other set, we have threads that are getting shorter, their last prompt is shorter than their first prompt. We see in Figure 4 that the prompts that get longer start shorter, and that the prompts that get shorter start longer:
Other works try to improve prompts by creating design guidelines (Liu and Chilton, 2022), auto- matically optimizing the prompts (Hao et al., 2022) or suggest prompt ideas the user (Brade et al., 2023; Mishra al., 2023).
The closest to ours is Xie et al. (2023), an analysis of large-scale prompt logs collected from multiple text-to-image systems. They do refer to "prompt sessions", which they identify with 30- minute timeout. However, they do not split the prompts by scene, nor examine the dynamics of certain linguistic features changing along the inter- action.
(thread(()) < threadeS1
mean (thrread()) ShroadCS2
Both sets converge towards similar lengths. may suggest that there is a specific range of "good" prompt lengths, not related to the starting point, and that human users converge to it, increasing or decreasing the length of the prompt adaptively depending on where they started. We observe sim- ilar trends in some of the other features too (see App. SH).
DiffusionDB (Wang et al., 2022) is a text-to- image dataset, containing prompts and images gen- erated by Stable Diffusion (Rombach et al., 2021). It does not contain indications to whether the user upscaled the image or not, and therefore not suitable for our purposes. Another existing text-to-image dataset is Simulacra Aesthetic Cap- tions (Pressman et al., 2022), containing prompts, images and ratings. It does not contain meta-data such as user-id or timestamps which make it un- suitable for our purposes. Another resource is the Midjourney 2022 data from Kaggle. 6. It contains raw data from the Midjourney Discord between June 20 and July 17, 2022. We did not use it but
9 Discussion
In Section 7, we examined two explanations for the observed systematic adaptation process. The sec- ond option, that the model causes users to "drift" to- wards its preferences, raises concerns about naively using human data for training. Human data (com- pared to synthetic data (Honovich et al., 2022; Wang et al., 2023)) is often perceived as most suit- able for training. The Midjourny data can be used
hhttps://wwe kaggle. wwwggll.coooooo/oe ht/dd/aatssss/ddttoo/.// midjourney-250k- c50

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scraped the data by ourselves, to obtain larger and more recent dataset of prompts and of a newer version of Midjourney.
clude the image files in our dataset, but only links to them.
A Acknowledgements
Limitations
This work was supported in part by the Israel Sci- ence Foundation (grant no. 2424/21).
Our results explain a relatively small proportion of the variance between the upscaled and non- upscaled prompts. Similarly, the effects we show are statistically significant, presenting a conceptual point, but not large. Users have different levels of experience and preferences, and therefore their prompts and decision upscale an image diverge. Future work will control for the content and quality of the prompts, in which case we expect to be able to explain a larger proportion of the variance.
References
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We suggest two possible explanations regarding the observed convergence. We do not decide be- tween them or quantify their effect. We do however show evidence supporting both, implying that both possibilities play a role.
As mentioned in $4.3, most of the threads are short, with one to two prompts. This is not surpris- ing, as not all the users spend time in improving their first prompt. That left us with 6578 threads of at least 4 prompts each, 2485 of at least 6, and 1214 of at least 8. This may not be sufficient for future analyses. We will share the code used to collect and process this dataset upon publication, it will be always possible expand this more. During our work on this paper, a newer ver- sion of Midjourney was released (v5). It is very likely that the updates to the model would affect the prompts too, and all the more so if we will analyze prompts from completely different system (we do successfully reproduce the results with Stable Diffusion data, see $F). However, we are not in- terested in specific values and "recipes" for good prompts. We only wish to point out the existence adaptation and convergence processes.
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Ethics Statement
We fully anonymize the data by removing user names and other user-specific meta-data. Upon publication, users will also have the option to re- move their prompts from our dataset through an email. Our manual sample did not find any of- fensive content in the prompts. The Midjourney Discord is an open community which allows oth- ers to use images and prompts whenever they are posted in public setting. Paying users do own all assets they create, and therefore we do not in-
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Shachar Don- Non-Yehiya, Leshem Choshen, and Omri Abend. 2022. PreQuEL: Quality estimation of ma- chine translation outputs advance. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11170-11 1183, Abu Dhabi, United Arab Emirates. Association for Computational Linguisties.

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Jonas Oppenlaender. 2022. A taxonomy of prompt modifiers for text-to-image generation.
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A Thread Examples
Martin J. Pickering and Simon Garrod. 2004. Toward a mechanistic psychology of dialogue. Behavioral and Brain Sciences, 27(2):16--190.
We provide threads examples with explanations. The full data is available at https: //gi thub. com/ Mhacharrdo/Mii-JJurrnie -or---d-oooor----lllilaaa-----
Martin Pickering and Simon Garrod. 2021. Under- standing Dialogue: Language Use and Social Inter- action. Cambridge University Press.
The following is an example for a prompts length thread.
John David Pressman, Katherine Crowson, and Simu- lacra Captions Contributors. 2022. Simulacra aes- thetic captions. Technical Report Version 1.0, Sta- bility AI. url https//gittub.com/I-PP//ii P/simulaaa- aesthetic-caption..
walking tiger from side simple vector clean lines logo 2d royal luxurious 4k
walking tiger from side simple vector clean lines logo 2d royal luxurious 4k in white back- ground
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners.
3. running tiger from side simple vector clean lines logo 2d royal luxurious 4k white back- ground png
Robin Rombach, Andreas Blattmann. Dominik Lorenz. Patrick Esser, and Björn Ommer. 2021. High- resolution image synthesis with latent diffusion mod- els.
4. jumping running tiger with open mouth from side simple vector clean lines logo 2d royal luxurious 4k gold and black with white back- ground
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma. Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bersstein, Alexan- der Berg, and Li Fei-Fei. 2015. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (I/CV), 115(3):11-2222.
The prompts describe the same main subject and scene, only small details are applied.
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannanch Hajishirzi. 2023. Self-instruct: Aligning language models with self-generated instructions.
The following prompts are not similar to cach other as the prompts from the previous thread are, but they do belong to one thread:
Zijie Wang. Evan Montoya, David Munechika, Haoyang Yang, Benjamin Hoover, and Duen Horng Chau. 2022. DiffusionDB: A large-scale prompt gallery dataset for exxttt--mmaee generative models. arXiv:2210. 14896 [cs].
cloaked man standing on a cliff looking at a nebula
destiny hunter, standing on a cliff, looking at blue and black star
Deanna Vilkes-Gibbe and Herbert H Clark. 1992. Coor- dinating beliefs in conversation. Journal Memory and Language, 3132)1183-194
cloaked hunter, standing on a cliff, looking at a blue and black planet
Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li. 2023. Better aligning text- to-image models with human preference. ArXiv, abs/2303.14420.
The following two prompts are of the same user, created one after another, but are not part of one thread:
The girl who is looking at the sky as rains in the gray sky
Yutong Xie, Zhaoying Pan, Jinge Ma, Luo Jie, and Qiaozhu Mei. 2023. A prompt log analysis of text- to-image generation systems. Proceedings of ACM Web Conference 2023, ACM.
And-

---

Length
Magic
Perplexity
Concreteness
Repeated
Sent Rate
Depth
Upscaled
15.52
0.101
2403
3.2692
0.035
13.15
6.13
Not
13,96
0.091
3081
3.2666
0.0313
11.94
5.951
value
1.0e 217
1.5e
64
1.2e
0.021
3.3e
40
1.3e 145
6.6e 87
Table rerun the experiment from $5.2, this time with prompts with default parameters only, effects from the original non-filtered experiment persist.
1. Rain in the gray sky, look at the sky, Bavarian with a sword on his back
DeBERTa-large (He et al., 2023). We use batch size 16, AdamW optimizer, learning rate 2e 5, weight decay 0.01, and 3 epochs.
Although the scene similar (rain, gray sky, a figure it looking at the sky), the main subject is intrinsically different (a girl male Bavarian with a sword).
We train each instance of the models on 2 CPU and 1 GPU. The image classifier took the longest to train, about 20 hours, probably due the time it takes load the images from their url links.
The following three prompts constitute thread:
We did not perform hyperparameters search, as we only wanted to state a conceptual claim.
1. the flash run
C Default Parameters Only
2. the flash, ezra miller, speed force
is possible that predefined parameters (see $3.1) have an effect on output acceptability. Therefore, we rerun the experiment from $5.2, this time with prompts with default parameters only. Filtering out prompts with any predefined parameters (i.c. non default parameters) leaves us with 147,236 236 prompts. In table 2 we see that the results are simi- lar to those of the non-filtered experiment, with all the effects from the original experiment preserved.
3. the flash running through the speed force
But the next prompt-
1. superman henry cavill vs the flash ezra miller movie
Is starting a new one, as both the main subjects (flash and superman) and scene (not rumning) are different.
The following prompts are not part of one thread:
D Applying our Method to the Captions
1. A man rides flying cat and swims on a snowy mountain
So far, used our methodology to examine the properties of the prompts between upscale and non- upscaled versions and along the interactions. We now examine whether our conceptual framework can used also for inferring the semantic proper- ties the generated images.
And-
1. A flying cat eats canned fish
The main subject is similar, but the scene is differ- ent.
We already have indication that the upscaled and non-upscaled images can be distinguished from each other ($6.1). However, using the images them- selves for the classification, aesthetics of the image (e.g., whether the people's faces look realistic or not) from its content (e.g.. what characters are in the image, what are they doing), nor to examine the linguistic features we already found to be relevant for the prompts.
B Classifier Training Details
cannot separate the
For the image classification we use Resnet18 model (11M parameters) (He et al., 2015) that was pretrained on ImageNet (Russakovsky et al., 2015). We use batch size 8, SGD optimizer, learning rate 0.001, momentum 0.9, and X epochs. The input to the model is the 4 images grid. We tried to take input the first image only, but it degraded the results.
For that, we represent each image with textual caption that describes it. To perform the analysis at scale,
automatically generated captions.
For the text classifier model, we use GPT-2 (1 parameters) (Radford al., 2019) with a classification head on top of it. The also experi- mented with RoBeRTa-large (Liu et al,, 2019) and
We use BLIP-2 (Li et al., 2023) generate cap- tion for the first image (out of the grid of images) associated with given prompt. We extract the same linguistics features we did for the prompts

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Length
Magic
Perplexity
Concreteness
Repeated
Sent Rate
Depth
Upscaled
9.292
0.048
356
3.095
0.013
9.290
5,832
Not
9.233
0.049
3.080
0.016
9.228
5.788
p value
8.8e
0.0002
1.7e 12
9.2e 29
1.1e
4.3e 7
4.de
Table The linguistic features values for the captions that match the upscaled and non-upsealed prompts. the features significant, including the concreteness score which was not significant for the prompts themselves. Compared to the prompis, the captions are shorter, have lower magic words ratio, lower perplexity, shorter sentences and smaller effects size. The magic words ratio and the repeated words ratio are lower for the non-upscaled captions. the opposite of the prompts case.
($5.2), and use the Mann-Whimmn U test on them. Table 3 we see that all the features were found to be significant, even the concreteness score that was not significant for the prompts. However, the effect sizes are smaller, and the direction of some of the effects is different. The magic words ratio and the repeated words ratio are both lower for the upscaled images compared to the non-upscaled, instead of higher like it was for the prompts.
divide the probability of a word appear in a prompt by its "regular" google appearance proba-
bility Pueneral (word). We say word is "magic word" if it appears least 1000 times at the dataset 1000 and the probability ratio is Pgeneral (word) > 100.
Pmidi
Piiidy (word) >
at least Pmndy
The full magic words list (total 175 words): realistic, style, logo, background, detailed, 8k, Lighting. 4k, ultra, cute, lighting, cinematic, hy- per, colors, photo, cartoon, Ray, anime, 3d, in- tricate, photorealistic, photography, super, Cine- matic, Reflections, illustration, render, futuristic, Tracing, Illumination, dinosaur, portrait, fantasy, dino, cyberpunk, neon, minimalist, Photography, mini, 8K, Screen, Grading, HD, colorful, Unreal, Engine, hyperrealistic, RTX, hd, 4K, Color, oc- tane, Beautiful, Volumetric, SSAO, unreal, Depth, RGB, realism, volumetric, Shaders, poster, Realis- tic, TXAA, CGI, Studio, minimalistic, 32k, beauti- fully, FKAA, Traced, VFX, Tone, DOF, SFX, Am- bient, Logo, tattoo, vibrant, Hyper, Soft, Lumen, Accent, VR, Mapping, AntiAliasing, Megapixel, Shadows, Occlusion, hyperdetailed, Incandescent, HDR, Diffraction, Optics, Chromatie, Aberration, insanely, Scattering, Backlight, Lines, Moody, Shading, Rough, Optical, curly, SuperResolution, ui, tshirt, vintage, ProPhoto, ultradetailed, ar, Fiber, OpenGLShaders, Glowing, Scan, ultrarealistic, UI- tra, v4, grading, Shimmering, ux, Tilt, PostPro- duction, Shot, Displacement, pixar, Cel, Edito- rial, GLSLShaders, Blur, wallpaper, hdr, Photo- shoot, ContreJour, sticker, Angle, occlusion, pastel, graded, Massive, 16k, watercolor, coloring, retro.
We can speculate that the smaller effects are due to the image captioning model, which was trained to generate captions in a relatively fixed length and style, similar to those of the training examples it was trained on. The length, tree-depth and the sentence rate seems consistent with what saw for the prompts. It is little odd, however, as while human user can choose to mention or not the color of the shoes that the kid in the scene is wearing, the shoes will have a color anyway, and the caption model would presumably handle it the same way. A possible explanation is that there is more con- tent in the upscaled images. For example, dragon and kid instead of dragon only. Another option is that the details in the upscaled images are more noteworthy. For example, if the kid has blue hair and not brown. As for the magic word ratio and the repeated words ratio, they may strengthen the hypothesis that their rise in the prompts is result of adaptation to the model's preferences; indeed, we do not see a similar effect in the captions. We defer deeper investigation of this issue further
research.
E Magic Words List
To find the magic words, we count the number of appearances of each word in the whole dataset, and normalize it by the total number of words (word). We then
F DiffusionDB Results
We repeat some of the experiments with data from the DiffusionDB dataset (Wang al., 2022), a text- to-image dataset of prompts and images generated by Stable Diffusion (Rombach et al., 2021). As discussed in $10, this dataset does not contain indi- cations as to whether the user upscaled the image
to obtain a probability query google-ngrams? to get notion of the gen- eral probability of cach word P Pgenerat (word), (word), and
Phttps://bookee wtwp//oook..o.ogee..e/e.o com/ngrams/

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730
0.196
630
600
21.5
Magic Words
0,192
Perpledty
550
21.0
500
20.5
430
20.0
0.188
400
Prompt
(a) Prompt Length
(h) Magic Words Ratio
(c) Perplexity
7.1
0,0475
Words Sentence
7.0
Repeated Werds
0,0450
8.9
0,0425
Dapth
0,0400
20
6.8
0,0075
5.7
19
0,5050
8.6
2
4
8
8
10
The 1thh Prompt
The Pth Prompt
The 7th Prompt
(d) Repeated Words Ratio
(e) Sentence Rate
(f) Depth
Figure 5: The thread dynamics experiment with the DiffusionDB dataset. Average value for each of the significant linguistic features (y-axis), as function of the prompt index (x-axis). Except for magic words ratio, the features remain approximately monotonous like in main experiment with the Midjourney dataset $3. Our features are relevant for the Stable Diffusion model too.
not, and therefore we can not it repro- duce the classifiers and the Mann-Whitney U test results ($6). We can however, use to reproduce the thread dynamics experiment.
We double the number of iterations looking at. In Figure $6 we see that the features remain approximately monotonous.
We again note that this analysis is noisy. Not only that the later iterations average only few prompts, they also possibly coming from dif- ferent distribution (the "long threads" distribution).
We take the first 250, 000 prompts of the 2M subset of the dataset. After cleaning (see $3.1), remain with 105, 644 prompts. We split threads ($4), resulting with 14, 927 threads, 1045 them contain at least prompts.
H Convergence Patterns for all the Features
We present results in $5. Except for the magic words ratio, all the features remain approx- imately monotonous like in our main experiment with the Midjourney dataset. Therefore, although not a logical necessity (see 10), it seems that most our features are relevant for the Stable Diffusion model too.
Like did in $6.3 with the prompt length, for each of the significant linguistic features we split the threads two groups by the first and last values. One group contains threads that their first value is higher than their last value, and the second threads that their first value is lower than their last.
G Longer Iterations
Like in the prompt length case, we see in Figure that the group that become longer start lower than the group that become shorter, and that they all go toward a narrower range.
In $6.3, we restricted our analysis to threads with at least 10 prompts to avoid the risk of mixed sig- nals. This restriction limits our ability to analyze longer threads, as there are few of them (there only 67 threads with at least 20 prompts, see also Figure $2). Here, we loosen this restriction and use threads with least 2 prompts, allowing the number averaged prompts at each index to vary.
This result is not trivial. For example, if we were divide all the stocks in the stock market into those that rose during the day and those that fell, not true that those that rose started lower and those that fell started higher. If that were the case, we would have a clear investment strategy - buy

---

2500
24
0.15
22
Megic Wards
2000
Perploxty
0.12
16
0.11
15
18
The Prompt
The 7th Prompt
The 7th Prompt
(a) Prompt Length
(b) Magic Words Ratio
(c) Perplexity
0.7
0.5
0.05
Words
16
8.3
0.04
14
6.1
13 The Ith Prompt
9
11
15
17
19
5
13 The fth Prompt
9
11
15
17
19
5
9
11
13
16
17
19
The Fth Prompt
(d) Repeated Words Ratio
(c) Sentence Rate
(f) Depth
Figure 6: Average value for each of the significant linguistic features (y-axis), function of the prompt index (x-axis). We double the number of iterations we are looking at, and loosen our restriction, allowing the number of averaged prompts at each
to vary. The features remain approximately monotonous, with some hallucinations. Most of the features go up (length, magic words ratio, repeated words ratio, sentence rate and tree depth) and the perplexity down. The users are not randomly trying different prompts until they reach good ones by chance, they are guided in certain direction.
only low-priced stocks.

---

28
getiing highar gelling
getting higher getting
geiling higher
1600
getting
Magic Words
0.18
Perploiity
1400
1200
0.18
1000
20
0.14
18
0.12
800
0.10
(a) Length
(b) Magic Words Ratio
(c) Perplexity
getting higher getting lower
getting higher
0.20
getling lower
Words Sentence
getting lower
100
Depth
Repasted
18
0.10
0.05
8.0
14
10
4
10
The 1th Prompt
The Ith Prompt
The fth Prompt
(d) Repeated Words Ratio
(c) Sentence Rate
(f) Depth
Figure 7: The groups that become longer start lower than the groups that become shorter, and they all go toward narrower range, implying convergence to a specific "good" range.


Reviews:
[{"review_id": "4fkfCQl3LD", "reviewer": "Reviewer_qxK5", "summary": "", "questions": "- What proportion of variance in prompting dynamics do the identified effects explain? How does controlling for user goals/experience affect this?\n- How well do the results generalize to other models besides Midjourney? Are there common patterns?\n- Can you further tease apart and quantify the relative impact of the two proposed explanation factors?\n- How do the dynamics look over a larger number of iterations beyond 10? Is there continued convergence?\n- How do your findings relate to human-AI conversations more broadly? Can similar effects be seen there?", "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": "We thank you for the deep reading and appreciate the attention to detail reflected in your review.\n\n* In this work we chose to look at threads in the wild, without the interference of instructing the annotators. We expect most of the variance to be explained by the users' goals and experience, which we do not control for. As mentioned in the limitations, we plan this for future work. \nFinding the proportion of the explained variance in these settings is not forthright in this case, as we do not expect to be able to predict the dynamics without these controls. Our most related finding to this in the paper is the text classifier results (section 6.1), which outperformed the random baseline by 8.2 accuracy points.  \n\n* It is very likely that the patterns between different systems (and even between different versions of the same system) will vary.  As mentioned in the limitations section, we are not interested in specific values for good prompts. We only wish to point out the existence of adaptation and convergence in the threads. However, we will add some experiments with the StableDB dataset (restricted to the limited metadata it contains).\n\n* We are not sure how to tease apart and quantify the two explanations, but if you have any ideas in mind we would be very happy to try. We would like to emphasize that our suggested features are probably not the only ones, so although they are indeed useful in showing that these explanations are present, they might not be suitable for quantitative examination.\n\n* When looking at 10 iterations, we are limiting our analysis to threads of at least 10 prompts, to avoid mixed signals (maybe longer threads tend to contain longer prompts? We do have these results however, and we will add them to the appendix). Notably, those also form the vast majority of interactions. With this restriction, when looking at longer interactions we will have only a few threads to examine (see fig. 2). Note that in the significant features test (6.2) we compared the first and last prompts, so it does take longer iterations into account.\n\n* Handling other human-model conversations systems with the exact same method is challenging. Unlike text-to-image systems, other conversational systems tend to include multiple topics and vary along the conversation. We can not assume that the content remains relatively similar between the iterations as we did with the text-to-image model. We aim to find the right method for analyzing general conversational systems in future work.", "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. Analyzes an important aspect of human-AI interaction dynamics that has received little prior attention. Understanding how users adapt their language when interacting with models over time provides useful insights.\n2. Provides the new Midjourney iterative prompt dataset as a contribution for the research community. This data enables further analysis of human-model prompting dynamics.\n3. Identifies several interesting patterns in how users change their prompts over iterations, such as increases in length, perplexity drops, and more concreteness. The trends indicate clear convergence effects.\n4. Makes an important cautionary point about potential biases that could arise from reusing human-AI data. The prompts likely become adapted to a particular model's preferences, rather than natural human language.\n5. The paper is generally clearly written and easy to follow. The introduction motivates the research question well. The method and analysis are adequately explained and presented.\n6. The scope of focusing on textual prompting for a single model is reasonably narrow. This enables an in-depth analysis of prompting dynamics for Midjourney.\n7. The analysis methodology using prompt feature analysis and thread dynamics seems appropriate and is able to uncover clear patterns in the data.", "reasons_to_reject": "1. The identified effects account for a relatively small portion of variance between prompts. Individual user goals and experience likely play a large role that is not controlled for.\n2. Only examines prompts from a single model, Midjourney. The results may not generalize well to other text-to-image models or conversational AI systems.\n3. Provides evidence for the two proposed explanations, but does not further tease apart or quantify their relative impacts on prompting dynamics.\n4. The results are over a limited set of 10 iterations. It is unclear if the patterns continue in the same direction over longer interactions.\n5. The scope focusing solely on textual prompts for a single model is quite narrow. The findings do not necessarily apply to broader human-AI conversations.\n6. While the identified patterns are interesting, they are not particularly surprising. The basic idea of users adaptively refining prompts matches intuitions.\n7. The methodology analyzes overall dataset patterns. It does not look at differences in prompting dynamics across individual users or prompt topics.", "questions_to_authors": "- What proportion of variance in prompting dynamics do the identified effects explain? How does controlling for user goals/experience affect this?\n- How well do the results generalize to other models besides Midjourney? Are there common patterns?\n- Can you further tease apart and quantify the relative impact of the two proposed explanation factors?\n- How do the dynamics look over a larger number of iterations beyond 10? Is there continued convergence?\n- How do your findings relate to human-AI conversations more broadly? Can similar effects be seen there?", "paper_topic_and_main_contributions": "This paper analyzes the dynamics of human prompts along iterative interactions with the Midjourney text-to-image model. The authors compile a dataset of 107K threads containing sequences of prompts and images generated by Midjourney. Their analysis reveals convergence patterns in prompt features like length, perplexity, and concreteness over the iterations. They suggest two explanations for this: (1) users adding omitted details, and (2) users adapting their language to match the model's preferences. They present evidence supporting both possibilities. The authors caution against reusing such data to further train models, as prompts become biased toward a particular model's preferences.", "excitement": 4, "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": {}}, {"review_id": "BP4HZh8RVv", "reviewer": "Reviewer_4xF9", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "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 for your positive review.\n\nWe gave two “definitions” of magic words in the paper (section 5.2):\n\n\n* Words that do not add any real content to the prompt\n* Words that can improve the aesthetics and attractiveness of images, according to the online community\n\nWhen claiming that the increased use of magic words is an indication to the second explanation (model adaptation), we mainly focus on the first definition. We observe that users add to their prompts words that do not add substantial new information, something that can not be explained by the first explanation (adding missed details).\n\nWe are not sure we fully understand the second point about language styles.  We interpreted it as asking human users to intentionally try several “styles” and then pick one rather than gradually levitate in a specific direction. This method will indeed be useful for finding what the model likes, but this is not our goal. We are interested in what the users are led to use organically. Please let us know if we did not understand correctly.", "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 detailed analysis of the prompts can facilitate understanding the user behavior during the image generation process.\n- The two explanations are reasonable and well-supported by the linguistic feature statistic.", "reasons_to_reject": "The explanation for adding omitted details is reasonable and convincing. This happens in many cross-modality tasks, such as image/video retrieval and generation. However, the evidence for the second point: Adopting Model-Like Language, is insufficient. \n1. Users do not know whether a word is a magic word or not. If the knowledge of the magic word is from an external source, like a community, the interactions, it's hard to say they learn the way to use magic words. The increase in the magic words is likely to be a part of the complemented information by the users and has no difference from the other complemented words.\n2. A learning process can be shown as the preference of a language style after trying different language styles. The change between different styles and the final convergence to a specific style can more sufficiently support the statement.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper analyzed the statistic of linguistic features of the prompts used in the image generation with MidJourney. Two user behavior patterns are proposed and the extracted statistic supports the assumption about the dynamics.", "excitement": 4, "reproducibility": 5, "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": "YhziuFHoPC", "reviewer": "Reviewer_jjW3", "summary": "", "questions": "- What is the intuitive meaning of perplexity in 5.2? If GPT-2 found the prompt to be more likely, does this mean it's more likely to be a \"human-like\" sentence [judging from the training corpus which should pre-date midjourney] rather than a prompt written in a dedicated \"midjourney style\"?", "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": "We thank the reviewer for their support, as well as the clear and accurate summary.\n\nWe will add in the camera ready experiments without any predefined parameters (so all set to default). \n\nThe main figure contains two thread examples, we will be happy to add more examples to the paper, including some “negative” examples to show when a new thread starts. \nWe chose the thresholds mostly manually – we tried different ones and looked at some prompts to decide whether it was better or not (prompts that were not included in the human annotation evaluation). Probably there are better choices, but as our human evaluation shows, they seem to be good enough to rely on for our analysis.\n\nOur intuition is that better perplexity means more model-like language, in contrast to human-like language.\nWe repeated this experiment with GPT-2-medium and GPT-NEO, to vary the model size and the training data, and obtained the same trend. We therefore conclude that this preference is shared across many models, possibly Midjourney among them.\n\nThe dataset and the code will be released upon acceptance; we hope this will facilitate reproducibility.", "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": "- This analysis is highly topical and very relevant for the wider availability and common usage of AI models, as well as how users interact with these models via the modality of text\n- The authors raise an important point about potential limitations when trying to harvest further datasets for model refinement - there is not necessarily an easy \"free lunch\" in bootstrapping a model from human interactions therewith.\n- This dataset could have useful applications across a wide range of tasks, including: usage as a \"contrastive\" objective to force greater diversity in model responses, a method to refine midjourney prompts for a particular frozen text-to-image model version, gauging bias (and user sensitivity to bias) in image generations for prompts containing/omitting certain sensitive specifications (including race, color, gender, etc.).\n- The paper is structured well and written clearly, both in terms of experimental design and result discussions.", "reasons_to_reject": "- Would like to see greater discussion about the impact of removing predefined parameters (3.1) - this potentially has an effect on output acceptability (e.g. higher chaos or noise could lead to greater adjustment of prompts), and it would be good to see a version of analysis with for e.g. only prompts with the same hyperparameters.\n- Why was 0.3 chosen as an IoU/Jaccard threshold, and why is BERTScore threshold of 0.9 used? It would be good to see illustrative examples at different thresholds to give an idea of how threads are split and what constituted a \"new thread\" in the analysis.", "questions_to_authors": "- What is the intuitive meaning of perplexity in 5.2? If GPT-2 found the prompt to be more likely, does this mean it's more likely to be a \"human-like\" sentence [judging from the training corpus which should pre-date midjourney] rather than a prompt written in a dedicated \"midjourney style\"?", "paper_topic_and_main_contributions": "This is a dataset and analysis paper that looks at human interactions with AI-assisted creative tools; specifically Midjourney for text-to-image generation. The authors find interesting results that there may be a feedback loop in play where humans are guided in their prompt design by model biases/preferences rather than their own goals. This is an interesting paper that studies a dynamic that is very relevant for societal ways of interacting with AI, and dives deep into the iterative dialog between users and the system. The authors ground their hypotheses in linguistic theory about the evolution/adaptation of language in discourse.", "excitement": 4, "reproducibility": 2, "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": {}}]

Author rebuttal:

Meta-review: All reviewers agree on the 4:4 scores for soundness and excitements and I can only agree with those reviews. The paper provides a novel and original contribution that the field can benefit from. 

Pros:
- overall, the paper is clearly written, well structured and comprehensive
- the contribution of the dataset is valuable to the community
- idea and methodology are novel and very timely
- the limitations and critical assessment is appreciated

Cons:
- most of the criticism is raised with respect to inaccuracies within the paper which have been clarified in the rebuttal
- without the data/code available during review, the reproducibility is limited

Overall, I agree with the reviewers and agree that the paper is moderately sound and exciting. What is missing for 5:5 made apparent by the many (minor) ambiguities that raise the questions of all reviews.
Decision: Accept-Main