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@@ -53,22 +53,25 @@ Main Columns:
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  - 'text' - the original prompt
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  - 'args' - predefined parameters (such as the aspect ratio, chaos and [more][myexample])
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  - 'channel_id' - the discord channel
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- 'userid' - an anonymous user id
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- 'timestamp' - a timestamp of the prompt creation
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- 'label' - Ture whether an image that was generated based on that prompt was upscaled, otherwise False.
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- 'id' - unique id of the prompt
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- 'url_png' - like to the generated images (a 4-grid version)
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- 'main_content' - prefix of the prompt, without trailing magic-words
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- 'concreteness' - concreteness score, based on the [this paper][concpaper]
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- 'repeat_words' - the occurrences of each word that appears more than once in the prompt, excluding stop words.
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- 'perplexity' - the perplexity GPT-2 assigns to each prompt.
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- 'word_len' - the number of words
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- 'caption_0-3' - captions that were generated by the BLIP-2 model
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- 'phase' - train/test split, as was used to train image/text classifiers
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- 'magic_ratio' - the percentage of words that were recognized as magic words in the prompt
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- 'thread_id' - the id of the thread
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- 'depth' - the max depth of a constituency parse tree of the prompt.
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-
 
 
 
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  [myexample]: https://docs.midjourney.com/docs/parameter-list "markdown more"
@@ -78,7 +81,10 @@ Main Columns:
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  ### Curation Rationale
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- <!-- Motivation for the creation of this dataset. -->
 
 
 
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  [More Information Needed]
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@@ -92,27 +98,8 @@ We randomly choose one of the ``newbies'' channels, where both new and experienc
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  #### Data Collection and Processing
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  <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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- #### Who are the source data producers?
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- <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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- [More Information Needed]
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- ### Annotations [optional]
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- <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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- #### Annotation process
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- <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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- [More Information Needed]
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- #### Who are the annotators?
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- <!-- This section describes the people or systems who created the annotations. -->
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  [More Information Needed]
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@@ -152,21 +139,3 @@ We split the prompts into threads automatically, and therefore there are some mi
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  primaryClass={cs.CL}
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  }
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- [More Information Needed]
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- ## Dataset Card Authors [optional]
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- [More Information Needed]
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- ## Dataset Card Contact
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- [More Information Needed]
 
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  - 'text' - the original prompt
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  - 'args' - predefined parameters (such as the aspect ratio, chaos and [more][myexample])
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  - 'channel_id' - the discord channel
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+ - 'userid' - an anonymous user id
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+ - 'timestamp' - a timestamp of the prompt creation
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+ - 'label' - Ture whether an image that was generated based on that prompt was upscaled, otherwise False.
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+ - 'id' - unique id of the prompt
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+ - 'url_png' - like to the generated images (a 4-grid version)
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+ - 'main_content' - prefix of the prompt, without trailing magic-words
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+ - 'concreteness' - concreteness score, based on the [this paper][concpaper]
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+ - 'word_len' - the number of words
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+ - 'repeat_words' - the occurrences of each word that appears more than once in the prompt, excluding stop words.
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+ - 'reapeat_words_ratio' - repeat_words / word_len
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+ - 'perplexity' - the perplexity GPT-2 assigns to each prompt.
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+ - 'caption_0-3' - captions that were generated by the BLIP-2 model
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+ - 'phase' - train/test split, as was used to train image/text classifiers
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+ - 'magic_ratio' - the percentage of words that were recognized as magic words in the prompt
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+ - 'thread_id' - the id of the thread
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+ - 'depth' - the max depth of a constituency parse tree of the prompt.
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+ - 'num_sent_parser' - the number of sentences in the prompt.
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+ - 'num_sent_parser_ratio' - num_sent_parser / word_len
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+ - 'words_per_sent' - word_len / num_sent_parser
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  [myexample]: https://docs.midjourney.com/docs/parameter-list "markdown more"
 
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  ### Curation Rationale
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+ This dataset was introduced as part of the paper: [Human Learning by Model Feedback: The Dynamics of Iterative Prompting with Midjourney][ourpaper].
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+ It contains real user prompts from interactions with the Midjourney Text-to-Image model. The prompts are organized into "threads of interaction", such that each thread contains a user's trails to create one target image.
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+
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+ [ourpaper]: https://arxiv.org/abs/2311.12131 "markdown our paper"
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  [More Information Needed]
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  #### Data Collection and Processing
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  <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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+ We split the prompts into threads automatically, see the paper for more details.
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+ In addition, we extract features (perplexity, sentence length, and more).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  [More Information Needed]
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  primaryClass={cs.CL}
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  }
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