Cited by 150Digital Media and Visual ArtInnovative Human-Technology InteractionDesign Education and Practice

The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index; 2 of the 61 sources its claims cite were checked for support.

Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation

Open access
Suh, Sangho, Chen, Meng, Min, Bryan, Li, Toby Jia-Jun, Xia, Haijun·2024·Proceedings of the CHI Conference on Human Factors in Computing Systems

Abstract

Thanks to their generative capabilities, large language models (LLMs) have become an invaluable tool for creative processes. These models have the capacity to produce hundreds and thousands of visual and textual outputs, offering abundant inspiration for creative endeavors. But are we harnessing their full potential? We argue that current interaction paradigms fall short, guiding users towards rapid convergence on a limited set of ideas, rather than empowering them to explore the vast latent design space in generative models. To address this limitation, we propose a framework that facilitates the structured generation of design space in which users can seamlessly explore, evaluate, and synthesize a multitude of responses. We demonstrate the feasibility and usefulness of this framework through the design and development of an interactive system, Luminate, and a user study with 14 professional writers. Our work advances how we interact with LLMs for creative tasks, introducing a way to harness the creative potential of LLMs.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

The paper proposes “Prompting for Design Space,” a framework that has LLMs first generate relevant dimensions and then dimension-guided responses so users can explore a structured design space rather than converge on a single output. It instantiates this as Luminate and reports a study with 14 professional writers, finding high creativity support (CSI 82.16) and perceived benefits for divergent thinking and design-space understanding.

Key findings

  • Luminate received an overall Creativity Support Index score of 82.16 (SD = 5.14), with strongest support for Exploration (M = 18.21, SD = 1.76), which participants also rated the most important factor (Avg. Factor Count = 4).
  • In the 25-minute study task, participants prompted on average 4.3 times (SD = 3.9, range = [1, 15]) and explored 13.8 responses (SD = 7.6, range = [4, 26]); all reported that multiple options from one prompt benefited brainstorming/ideation, and the tool scored 9/10 (SD = 1.1) on tracking different ideas and exploring many ideas/options/outcomes.
  • All participants agreed generated dimensions helped them reflect on important task/topic factors (6 Strongly Agree, 8 Agree); dimensions were rated accurate (4 Strongly Agree, 6 Agree, 3 Neutral, 1 Disagree), useful (7 Strongly Agree, 7 Agree), and relevant (4 Strongly Agree, 9 Agree, 1 Neutral).
  • In a follow-up in-the-wild study, 8 participants used Luminate for about 2 weeks and still preferred it for brainstorming and creative tasks; in the initial study, 10/14 preferred Luminate for creative writing.

Methods

  • The authors designed and developed Luminate using React, Editor.js, and d3.js, with OpenAI’s text-davinci-003 API. The pipeline first prompts the LLM to generate categorical (ordinal and nominal) dimensions and values, then randomly selects one value per dimension to form requirements and generates responses in parallel.
  • A user study recruited 14 professional writers (9F, 5M; M age = 30, SD = 7.7; M writing experience = 7.3 years, SD = 8.4) via Upwork. The 80-minute online study included a pre-study survey, 25-minute interface tutorial, 25-minute creative writing task, and post-study survey plus exit interview. Luminate was configured for up to 5 nominal dimensions with up to 8 labels and up to 3 ordinal dimensions with 5 levels; responses were limited to 150 words and 40 responses per prompt.
  • Analysis combined Creativity Support Index scores, survey ratings, interaction logs, think-aloud observations, and semi-structured interviews, followed by an in-the-wild study with 8 participants over about 2 weeks.

Limitations

  • Stated in the paper: the study fixed response length at 150 words and response count at 40, which may have affected perceived value; it lacked testing with diverse user groups such as non-professional writers; and it lacked rigorous comparison with existing AI tools, relying instead on participants’ self-reported comparison with ChatGPT.
  • Evident from the text: the controlled study involved limited exposure (25–50 minutes), and some participants found 40 responses overwhelming or suggested fewer/simpler initial outputs; dimension quality also varied, with examples such as “creativity” appearing as an ordinal dimension and “gender” as a nominal dimension that one user did not find useful.

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Registry

Crossref · 10.1145/3613904.3642400

Cites

72 of 72 references verified0 citation errors · 0 not checkable

Statistics

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Figures

Image 1
From the PDF
Figure 2 An example showing the benefits of exposing design space to users. By generating relevant dimensions for a task or topic, we can inform users about many dimensions and [ values ] they can consider - e.g., plot complexity : [ linear , twisting , ...], genre : [ fantasy , adventure , ...]. If all the possible dimensions and values are used to generate responses, the responses will cover many sub-spaces within the overall design space and reveal to users a space of possible responses they can generate with LLMs.
Figure 2 An example showing the benefits of exposing design space to users. By generating relevant dimensions for a task or topic, we can inform users about many dimensions and [ values ] they can consider - e.g., plot complexity : [ linear , twisting , ...], genre : [ fantasy , adventure , ...]. If all the possible dimensions and values are used to generate responses, the responses will cover many sub-spaces within the overall design space and reveal to users a space of possible responses they can generate with LLMs.From the PDF
Figure 3 Luminate interface consists of two main sections: (A) text editor and (F) exploration view. Users can (B) type and use various (C) text styles (e.g., Title, normal text). They have the option to (D) input a prompt (e.g., ) to LLM and view (E) one of the generated responses in the text editor. (F) In the exploration view, users can observe (J) a cluster of responses and (H) dimensions. At first, no dimension is selected, to invite users to explore the design space. Users can add dimensions to the axes (I) and arrange responses by their dimension values (see Fig. 5). For optimal experience, we promptly display (G) dimensions as soon as they are generated, allowing users to examine them while responses are being generated. Users can click (K) semantic level icons to adjust the zoom scale and view responses at different levels of detail (see Fig. 8).
Figure 3 Luminate interface consists of two main sections: (A) text editor and (F) exploration view. Users can (B) type and use various (C) text styles (e.g., Title, normal text). They have the option to (D) input a prompt (e.g., ) to LLM and view (E) one of the generated responses in the text editor. (F) In the exploration view, users can observe (J) a cluster of responses and (H) dimensions. At first, no dimension is selected, to invite users to explore the design space. Users can add dimensions to the axes (I) and arrange responses by their dimension values (see Fig. 5). For optimal experience, we promptly display (G) dimensions as soon as they are generated, allowing users to examine them while responses are being generated. Users can click (K) semantic level icons to adjust the zoom scale and view responses at different levels of detail (see Fig. 8).From the PDF
Figure 4 Technical pipeline of Prompting for Design Space : The pipeline consists of two LLM prompting steps. (A) The first LLM step feeds the inputs from the text editor into the LLM API call to generate dimensions and their values as a JSON object. (B) Luminate transforms the generated object to form a list of requirements per response by randomly selecting a value from each dimension. The second LLM step feeds each list into separate LLM API calls and generates all responses in parallel. (C) Generated responses are then visualized in Luminate's response space.
Figure 4 Technical pipeline of Prompting for Design Space : The pipeline consists of two LLM prompting steps. (A) The first LLM step feeds the inputs from the text editor into the LLM API call to generate dimensions and their values as a JSON object. (B) Luminate transforms the generated object to form a list of requirements per response by randomly selecting a value from each dimension. The second LLM step feeds each list into separate LLM API calls and generates all responses in parallel. (C) Generated responses are then visualized in Luminate's response space.From the PDF
Figure 5 Dimension selection: upon (1) selecting a dimension ( Mood ) for the x-axis, responses (2) reposition to vertically align to their dimension values ( Romantic , Somber , Cheerful , Vengeful ). (3) Selecting a dimension for the y-axis ( Tone ) additionally repositions the responses to align horizontally to their dimension values ( Motivating , Hopeful , Inspiring , Peaceful ).
Figure 5 Dimension selection: upon (1) selecting a dimension ( Mood ) for the x-axis, responses (2) reposition to vertically align to their dimension values ( Romantic , Somber , Cheerful , Vengeful ). (3) Selecting a dimension for the y-axis ( Tone ) additionally repositions the responses to align horizontally to their dimension values ( Motivating , Hopeful , Inspiring , Peaceful ).From the PDF
Figure 6 Two methods for generating similar responses: Method#1: (A) Users can click the button on the node to generate similar responses. Method#2: (B) When nodes are filtered on the canvas, the user can click the button to generate more nodes to be added to the filtered subspace; (C) newly generated nodes join the canvas and align themselves to their dimension values.
Figure 6 Two methods for generating similar responses: Method#1: (A) Users can click the button on the node to generate similar responses. Method#2: (B) When nodes are filtered on the canvas, the user can click the button to generate more nodes to be added to the filtered subspace; (C) newly generated nodes join the canvas and align themselves to their dimension values.From the PDF
Figure 7 Generating new responses via user-defined dimension: Users can generate new responses by (A) adding new dimensions to their exploration space. Upon entering their desired dimension in the filter bar, Luminate generates (B) values (e.g., Victorian , Medieval , Colonial , Apocalyptic ) for the desired dimension ( Time Period ) and revises all existing responses to include one of the value for that response. Once complete, users can use the new dimension to search, filter, and cluster the revised responses.
Figure 7 Generating new responses via user-defined dimension: Users can generate new responses by (A) adding new dimensions to their exploration space. Upon entering their desired dimension in the filter bar, Luminate generates (B) values (e.g., Victorian , Medieval , Colonial , Apocalyptic ) for the desired dimension ( Time Period ) and revises all existing responses to include one of the value for that response. Once complete, users can use the new dimension to search, filter, and cluster the revised responses.From the PDF
Figure 8 Semantic zoom: The amount of information users see changes with zoom scale. As shown, as users zoom in, the displayed information transitions from title to keyword, then to summary and to full text. Users can double click anywhere on canvas to zoom directly into the full text level or zoom out to the dot level from the full text level, for efficient navigation of the space.
Figure 8 Semantic zoom: The amount of information users see changes with zoom scale. As shown, as users zoom in, the displayed information transitions from title to keyword, then to summary and to full text. Users can double click anywhere on canvas to zoom directly into the full text level or zoom out to the dot level from the full text level, for efficient navigation of the space.From the PDF
Figure 9 Search features: To address the challenge of locating the desired response with specific properties, Luminate offers two search methods. With (a), users can search for responses containing specific words such as ' love and peace . ' As shown, instead of completely removing the non-corresponding responses, we reduce their opacity, to preserve their spatial information while making the corresponding responses stand out. With (b), users can filter for (A) bookmarked nodes or by (B) dimension values using the dropdown. Alternatively, as shown, users can also (C) click on the dimension value ( ) to apply the filter. Somber
Figure 9 Search features: To address the challenge of locating the desired response with specific properties, Luminate offers two search methods. With (a), users can search for responses containing specific words such as ' love and peace . ' As shown, instead of completely removing the non-corresponding responses, we reduce their opacity, to preserve their spatial information while making the corresponding responses stand out. With (b), users can filter for (A) bookmarked nodes or by (B) dimension values using the dropdown. Alternatively, as shown, users can also (C) click on the dimension value ( ) to apply the filter. SomberFrom the PDF
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Image 12
From the PDF

Verification

Claims the paper makes

72 Own result · 41 Own interpretation · 32 Not checked yet · 6 Own method · 2 No basis given · 1 Supported so far · 1 Sources not found

Verify all 58 references — free account
  • Probably not a substantial claim. 83
  • It may state two separate claims as one. 40
  • The quote only partly states this claim. 3
109 of 109
  • The paper relies on this without support.

    The benefits of design space thinking and dimensional reasoning observed in human design contexts transfer to LLM-assisted creative writing.

    “Motivated by the benefits of design space thinking in creative processes - (1) avoiding fixation [36, 37] and (2) enabling systematic generation and evaluation of ideas [23, 50], we argue that generative AI models should assist in the generation of the design space rather than individual artifacts at the early stage of the creative process to empower users and harness the creative potential of AI.”
  • The paper relies on this without support.

    The design space of a creative writing task can be adequately represented by a set of categorical dimensions and their values generated by an LLM.

    “Luminate takes that prompt and instructs an LLM to generate a pre-defined number (e.g., 5) of categorical dimensions and corresponding values (e.g., Wave Intensity : [ rolling waves , gentle ripples ]).”
  • The paper relies on this without support.

    LLMs excel at quickly generating information.

    “As generative models, LLMs excel at quickly generating information.”
  • The paper relies on this without support.

    Humans are slower in producing a variety of responses (that vary in tone, composition, etc.) but excel at comprehending a broader context and the requirements of the tasks at hand.

    “Humans, on the other hand, are slower in producing a variety of responses (that vary in tone, composition, etc.) but excel at comprehending a broader context and the requirements of the tasks at hand.”
  • The paper relies on this without support.

    Humans can judge which response is best suited for a given task.

    “They can judge which response is best suited for a given task.”
  • The paper relies on this without support.

    The black-box nature of LLMs presents a major challenge for users when it comes to instructing the model to produce the desired output.

    “Additionally, the black-box nature of LLMs presents a major challenge for users when it comes to instructing the model to produce the desired output.”
  • The paper relies on this without support.

    Creative writers rarely, if at all, need or want a less creative artifact.

    “Yet, creative writers rarely, if at all, need or want a less creative artifact.”
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Citations

Cites

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Research works printed but not linked: 9

  1. [n. d.]. Futurepedia. https://www.futurepedia.io/ Last accessed 27 August 2023.
  2. [n. d.]. Upwork. https://www.upwork.com/. Accessed: September 13, 2023.
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  4. 10.18653/v1 · DOI
  5. Edward De Bono. 1970. Lateral thinking. New York (1970), 70. https://www.kioulanis.gr/rivips/images/Lateral_thinking.pdf
  6. Chris Heape. 2007. The Design Space: the design process as the construction exploration and expansion of a conceptual space. (2007). https://www.semanticscholar.org/paper/The-Design-Space%3A-the-design-process-as-the-and-of-Heape/40b912badea3b575a8f4bde95df4f83a4427ab78
  7. Jeyakumar Muthukumarasamy and John T Stasko. 1995. Visualizing program executions on large data sets using semantic zooming. https://doi.org/10.5555/832277.834333 · DOI
  8. 10.1109/VL · DOI
  9. Daijin Yang, Yanpeng Zhou, Zhiyuan Zhang, Toby Jia-Jun Li, and Ray LC. 2022. AI as an Active Writer: Interaction strategies with generated text in human-AI collaborative fiction writing. In Joint Proceedings of the ACM IUI Workshops. https://hai-gen.github.io/2022/papers/paper-HAIGEN-YangDaijin.pdf

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Metadata

Authors
Suh, S., Chen, M., Min, B., Li, T. J.-J., & Xia, H.
Affiliations
  • University of California San Diego (US)
  • University of Notre Dame (US)
Venue
Proceedings of the CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2024-05-11
Pages
1-26
OpenAlex
W4396832024
Cited by
148
References
60
Citations by year
2026: 62 · 2025: 76 · 2024: 10
FWCI
57.77
Type
conference paper
Language
en
Open access
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
  • Physical Sciences › Engineering › Mechanical Engineering › Design Education and Practice
Keywords
large language models, human-AI co-creation, design space exploration, creative writing, structured generation, interactive system design, creative process

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