Challenge: a corpus of image edit requests is elicited for real world images, and an annotation framework is developed . evaluators evaluate crowd-sourced annotation as a means of efficiently creating a sizable corpus at a reasonable cost.
Approach: They propose a natural language interface for interacting with an image editing program . they propose an annotation framework for understanding natural language requests .
Outcome: The proposed tool interprets image edit requests and maps them to actionable commands.

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Challenge: avrahami et al., 2022b,a): natural language instructions are often underspecified, requiring models to uncover their implicit meaning.
Approach: They propose to use paired data to model the implicit meaning of instructions . they also propose to ground the model to localize where the edit has to be performed .
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Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions (L18-1)

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Challenge: a crowdsourcing study has been conducted to generate rich textual descriptions of human faces . the aim is to investigate how users describe images of human face images .
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WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse (D18-1)

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Challenge: a corpus of 43 million atomic edits is available for Wikipedia edit history . edits are instances in which a human editor has inserted a single contiguous phrase into, or deleted a contigous phrase from, an existing sentence.
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Building The First English-Brazilian Portuguese Corpus for Automatic Post-Editing (2020.coling-main)

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Challenge: Existing corpus for automatic post-editing of English and Brazilian Portuguese is limited.
Approach: They introduce a corpus for Automatic Post-Editing of English and Brazilian Portuguese.
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The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)

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Challenge: Until recently, language descriptions were available in paper form only, with indexes as the only search aid.
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Adjusting Image Attributes of Localized Regions with Low-level Dialogue (2020.lrec-1)

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Challenge: Image editing is time-consuming and requires a wide assortment of features and combinations of these features to achieve a desired effect.
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A Corpus for Reasoning about Natural Language Grounded in Photographs (P19-1)

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Challenge: a dataset for visual reasoning with natural language and images is available.
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Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation (2021.acl-long)

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Challenge: Edited media frames are structured annotations with respect to intents, emotional reactions, attacks on individuals, and the implications of disinformation.
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Corpus Considerations for Annotator Modeling and Scaling (2024.naacl-long)

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Challenge: Recent trends in natural language processing and annotation tasks emphasize individual perspectives . annotator models that rely on a single ground truth may disregard valuable minority perspectives omissions .
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Editing Large Language Models: Problems, Methods, and Opportunities (2023.emnlp-main)

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Challenge: Recent advances in model editing for LLMs have created challenges and opportunities for the community.
Approach: They propose to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs.
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