Effidit: An Assistant for Improving Writing Efficiency

Shi, Shuming; Zhao, Enbo; Bi, Weihong; Cai, Deng; Cui, Leyang; Huang, Xinting; Jiang, Haiyun; Tang, Duyu; Song, Kaiqiang; Wang, Longyue; Huang, Chenyan; Huang, Guoping; Wang, Yan; Li, Piji

doi:10.18653/v1/2023.acl-demo.49

Cited by 2 publications

(6 citation statements)

References 0 publications

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“…Often, interaction with the underlying system takes place in the writing area where users create text. This supports the selection of the existing writing [57,223] and/or seamless integration of output from the system [58,148]. Alternatively, a design might choose to isolate the interaction with AI as a separated UI element, such as through a sidebar.…”

Section: Ui -Interaction Metaphormentioning

confidence: 97%

“…For larger datasets (around tens of 1000s of examples) we denote this as large [56,207,257]. For models that undergo extensive large-scale pre-training, we categorized data used in this process as extremely large to indicate a dataset of millions of examples [43,223,228,276] or more. We also included an unknown if the paper did not explicitly mention the dataset used for training [180,192,238].…”

Section: Dimensions and Codesmentioning

confidence: 99%

“…A foundation model is "any model that is trained on broad data that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks" [17]. These models can perform a wide range of tasks out of the box, learn from a few examples to provide tailored support to users, and be further fine-tuned for specific downstream task(s) [148,223,254,258].…”

Section: Dimensions and Codesmentioning

confidence: 99%

“…In the case of tool, a model may access external software or third-party APIs to perform tasks like search, translation, or calculator, or even setting calendar events on behalf of users [43,180,267,272]. Data refers to external datasets or resources, such as information stored in a database, external knowledge repositories, or any other structured/unstructured data sources that the models might leverage to provide writing assistance [133,223,230,276].…”

Section: Dimensions and Codesmentioning

confidence: 99%

“…Retrieval problems take the input from a user as a query (e.g., keywords) and search in a knowledge base or dataset for relevant information. Such problems may involve ranking the available data based on its relevance and similarity to the input but do not necessarily include the generation of new text beyond what is available in the knowledge base [37,223,232].…”

Section: Dimensions and Codesmentioning

confidence: 99%

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A Design Space for Intelligent and Interactive Writing Assistants

Lee,

Gero,

Chung

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through a large community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions (i.e., fundamental components of an aspect) and codes (i.e., potential options for each dimension) by systematically reviewing 115 papers. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the envisioning and design of new writing assistants.

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