Carrie Jun Cai
My research aims to make artificial intelligence systems usable to human beings, so that human-AI interactions are more productive, enjoyable, and fair. I believe AI systems should be designed to augment human agency, and thus approach this process by considering the capabilities and limits of human intelligence. Before joining Google, I did my PhD research in the User Interface Design group at MIT, where I built "wait-learning" tools to help people practice desired skills in short chunks while waiting, thereby making use of fleeting moments in the day.
Authored Publications
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"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output
Michael Xieyang Liu
Frederick Liu
Alex Fiannaca
Terry Koo
In Extended Abstract in ACM CHI Conference on Human Factors in Computing Systems (CHI EA '24), ACM (2024), pp. 9 (to appear)
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Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow specific formats or standards. In this work, we surveyed 51 experienced industry professionals to understand the range of scenarios and motivations driving the need for output constraints from a user-centered perspective. We identified 134 concrete use cases for constraints at two levels: low-level, which ensures the output adhere to a structured format and an appropriate length, and high-level, which requires the output to follow semantic and stylistic guidelines without hallucination. Critically, applying output constraints could not only streamline the currently repetitive process of developing, testing, and integrating LLM prompts for developers, but also enhance the user experience of LLM-powered features and applications. We conclude with a discussion on user preferences and needs towards articulating intended constraints for LLMs, alongside an initial design for a constraint prototyping tool.
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Generative Agents: Interactive Simulacra of Human Behavior
Joon Sung Park
Joseph C. O'Brien
Percy Liang
Michael Bernstein
Proceedings of UIST 2023, ACM (2023)
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Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.
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Programming with a Programming Language: Challenges and Opportunities for Designing Developer Tools for Prompt Programming
Alex Fiannaca
Chinmay Kulkarni
Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA ’23), ACM, Hamburg, Germany (2023) (to appear)
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Existing tools for prompt programming provide little support to prompt programmers. Consequently, as prompts become more complex, they can be hard to read, understand, and edit. In this work, we draw on modern integrated development environments for traditional programming to improve the editor experience of prompt programming. We describe methods for understanding the semantically meaningful structure of natural language prompts in the absence of a rigid formal grammars, and demonstrate a range of editor features that can leverage this information to assist prompt programmers. Finally, we relate initial feedback from design probe explorations with a set of domain experts and provide insights to help guide the development of future prompt editors.
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PromptInfuser: Bringing User Interface Mock-ups to Life with Large Language Model Prompts
Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery (to appear)
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Large Language Models have enabled novices without machine learning (ML) experience to quickly prototype ML functionalities with prompt programming. This paper investigates incorporating prompt-based prototyping into designing functional user interface (UI) mock-ups. To understand how infusing LLM prompts into UI mock-ups might affect the prototyping process, we conduct a exploratory study with five designers, and find that this capability might significantly speed up creating functional prototypes, inform designers earlier on how their designs will integrate ML, and enable user studies with functional prototypes earlier. From these findings, we built PromptInfuser, a Figma plugin for authoring LLM-infused mock-ups. PromptInfuser introduces two novel LLM-interactions: input-output, which makes content interactive and dynamic, and frame-change, which directs users to different frames depending on their natural language input. From initial observations, we find that PromptInfuser has the potential to transform the design process by tightly integrating UI and AI prototyping in a single interface.
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The Prompt Artists
Stefania Druga
Alex Fiannaca
Pedro Vergani
Chinmay Kulkarni
Creativity and Cognition 2023 (2023)
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In this paper, we present the results of a study examining the art practices, artwork, and motivations of prolific users of the latest generation of text-to-image models. Through interviews, observations, and a survey, we present a sampling of the artistic styles, and describe the developed community of practice. We find that: 1) the text prompt and resulting image collectively can be considered the art piece (prompts as art), and 2) prompt templates (prompts with “slots” for others to fill in with their own words) are developed to create generative art pieces. We also find that this community’s premium on unique outputs leads to artists seeking specialized vocabulary to produce distinctive art pieces (e.g., by going to architectural blogs), while others look for “glitches” in the model that can turn into artistic styles in their own right. From these findings, we outline specific implications for design.
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Prompt-based Prototyping with Large Language Models
Ellen Jiang
Edwin Toh
Aaron Michael Donsbach
ACM CHI case study track (2022)
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Prototyping is notoriously difficult to do with machine learning (ML), but recent advances in large language models may lower the barriers to people prototyping with ML, through the use of natural language prompts. This case study reports on the real-world experiences of industry professionals (e.g. designers, program managers, front-end developers) prototyping new ML-powered feature ideas via prompt-based prototyping. Through interviews with eleven practitioners during a three-week sprint and a workshop, we find that prompt-based prototyping reduced barriers of access by substantially broadening who can prototype with ML, sped up the prototyping process, and grounded communication between collaborators. Yet, it also introduced new challenges, such as the need to reverse-engineer prompt designs, source example data, and debug and evaluate prompt effectiveness. Taken together, this case study provides important implications that lay the groundwork toward a new future of prototyping with ML.
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Social Simulacra: Creating Populated Prototypes for Social Computing Systems
Joon Sung Park
Lindsay Popowski
Percy Liang
Michael S. Bernstein
Proceedings of UIST 2022, ACM (2022) (to appear)
Discovering the Syntax and Strategies of Natural Language Programming with Generative Language Models
Aaron Michael Donsbach
Edwin Toh
Ellen Jiang
CHI (2022)
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In this paper, we present a natural language code synthesis tool, GenLine, backed by a large generative language model and a set of task-specific prompts. To understand the user experience of natural language code synthesis with these types of models, we conducted a user study in which participants applied GenLine to two programming tasks. Our results indicate that while natural language code synthesis can sometimes provide a magical experience, participants still faced challenges. In particular, participants felt that they needed to learn the model’s "syntax,'' despite their input being natural language. Participants also faced challenges in debugging model input, and demonstrated a wide range of variability in the scope and specificity of their requests. From these findings, we discuss design implications for future natural language code synthesis tools built using generating language models.
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The Design Space of Generative Models
Jess Scon Holbrook
Chinmay Kulkarni
NeurIPS 2022 Human-Centered AI Workshop (2022) (to appear)
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Card et al.’s classic paper "The Design Space of Input Devices" established the value of design spaces as a tool for HCI analysis and invention. We posit that developing design spaces for emerging pre-trained, general AI models is necessary for supporting their integration into human-centered systems and practices. We explore what it means to develop an AI model design space by proposing two design spaces relating to pre-trained AI models: the first considers how HCI can impact pre-trained models (i.e., interfaces for models) and the second considers how pre-trained models can impact HCI (i.e., models as an HCI prototyping material).
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Onboarding Materials as Cross-functional Boundary Objects for Developing AI Assistants
Lauren Wilcox
Samantha Winter
Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, ACM (2021) (to appear)
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Deep neural networks (DNNs) routinely achieve state-of-the-art performance in a wide range of tasks. This case study reports on the development of onboarding (i.e., training) materials for a DNN-based medical AI Assistant to aid in the grading of prostate cancer. Specifically, we describe how the process of developing these materials deepened the team's understanding of end-user requirements, leading to changes in the development and assessment of the underlying machine learning model. In this sense, the onboarding materials served as a useful boundary object for a cross-functional team. We also present evidence of the utility of the subsequent onboarding materials by describing which information was found useful by participants in an experimental study.
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