# User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models

arXiv: https://arxiv.org/abs/2312.06826 · HTML: https://mlsanity.com/paper/2312.06826/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 15 of 15)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [Scalable Extraction of Training Data from (Production) Language Models](https://mlsanity.com/paper/2311.17035/) (2023)
- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)
- [Fundamental Limitations of Alignment in Large Language Models](https://mlsanity.com/paper/2304.11082/) (2023)
- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [Explanations Can Reduce Overreliance on AI Systems During Decision-Making](https://mlsanity.com/paper/2212.06823/) (2022)
- [Iterative Human and Automated Identification of Wildlife Images](https://mlsanity.com/paper/2105.02320/) (2021)
- [On the Influence of Explainable AI on Automation Bias](https://mlsanity.com/paper/2204.08859/) (2022)
- [Learning When to Advise Human Decision Makers](https://mlsanity.com/paper/2209.13578/) (2022)
- [Overcoming Algorithm Aversion: A Comparison between Process and Outcome Control](https://mlsanity.com/paper/2303.12896/) (2023)

## Built on by (most cited first, 1 of 1)

- [CURATRON: Complete and Robust Preference Data for Rigorous Alignment of Large Language Models](https://mlsanity.com/paper/2403.02745/) (2024)

## Abstract (arXiv)

While there is significant interest in using generative AI tools as general-purpose models for specific ML applications, discriminative models are much more widely deployed currently. One of the key shortcomings of these discriminative AI tools that have been already deployed is that they are not adaptable and user-friendly compared to generative AI tools (e.g., GPT4, Stable Diffusion, Bard, etc.), where a non-expert user can iteratively refine model inputs and give real-time feedback that can be accounted for immediately, allowing users to build trust from the start. Inspired by this emerging collaborative workflow, we develop a new system architecture that enables users to work with discriminative models (such as for object detection, sentiment classification, etc.) in a fashion similar to generative AI tools, where they can easily provide immediate feedback as well as adapt the deployed models as desired. Our approach has implications on improving trust, user-friendliness, and adaptability of these versatile but traditional prediction models.