# Exploring the Innovation Opportunities for Pre-trained Models

arXiv: https://arxiv.org/abs/2505.15790 · HTML: https://mlsanity.com/paper/2505.15790/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 81 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 22 of 22)

- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement](https://mlsanity.com/paper/2403.04014/) (2024)
- [DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing](https://mlsanity.com/paper/2401.10880/) (2024)
- [CreativeConnect: Supporting Reference Recombination for Graphic Design Ideation with Generative AI](https://mlsanity.com/paper/2312.11949/) (2023)
- [ChaCha: Leveraging Large Language Models to Prompt Children to Share Their Emotions about Personal Events](https://mlsanity.com/paper/2309.12244/) (2023)
- [How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?](https://mlsanity.com/paper/1909.03486/) (2019)
- [Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook](https://mlsanity.com/paper/2301.12243/) (2023)
- [CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models](https://mlsanity.com/paper/2402.15265/) (2024)
- [Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience](https://mlsanity.com/paper/2302.10395/) (2023)
- [Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design](https://mlsanity.com/paper/2303.02884/) (2023)
- [Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia](https://mlsanity.com/paper/2105.05849/) (2021)
- [fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks](https://mlsanity.com/paper/2302.11703/) (2023)
- [Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models](https://mlsanity.com/paper/2310.02161/) (2023)
- [Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI](https://mlsanity.com/paper/2308.07213/) (2023)
- [Canvil: Designerly Adaptation for LLM-Powered User Experiences](https://mlsanity.com/paper/2401.09051/) (2024)
- [C2Ideas: Supporting Creative Interior Color Design Ideation with Large Language Model](https://mlsanity.com/paper/2401.12586/) (2024)
- [ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12](https://mlsanity.com/paper/2402.04975/) (2024)
- [How Do UX Practitioners Communicate AI as a Design Material? Artifacts, Conceptions, and Propositions](https://mlsanity.com/paper/2305.17389/) (2023)
- [Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit](https://mlsanity.com/paper/2402.13437/) (2024)
- [DanceGen: Supporting Choreography Ideation and Prototyping with Generative AI](https://mlsanity.com/paper/2405.17827/) (2024)
- [PhotoScout: Synthesis-Powered Multi-Modal Image Search](https://mlsanity.com/paper/2401.10464/) (2024)

## Abstract (arXiv)

Innovators transform the world by understanding where services are successfully meeting customers' needs and then using this knowledge to identify failsafe opportunities for innovation. Pre-trained models have changed the AI innovation landscape, making it faster and easier to create new AI products and services. Understanding where pre-trained models are successful is critical for supporting AI innovation. Unfortunately, the hype cycle surrounding pre-trained models makes it hard to know where AI can really be successful. To address this, we investigated pre-trained model applications developed by HCI researchers as a proxy for commercially successful applications. The research applications demonstrate technical capabilities, address real user needs, and avoid ethical challenges. Using an artifact analysis approach, we categorized capabilities, opportunity domains, data types, and emerging interaction design patterns, uncovering some of the opportunity space for innovation with pre-trained models.