# A Large Language Model-based Computational Approach to Improve Identity-Related Write-Ups

arXiv: https://arxiv.org/abs/2312.16659 · HTML: https://mlsanity.com/paper/2312.16659/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 27 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Do Prompt-Based Models Really Understand the Meaning of their Prompts?](https://mlsanity.com/paper/2109.01247/) (2021)
- [Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models](https://mlsanity.com/paper/1804.09299/) (2018)
- [Graphologue: Exploring Large Language Model Responses with Interactive Diagrams](https://mlsanity.com/paper/2305.11473/) (2023)
- [Bayesian Prompt Learning for Image-Language Model Generalization](https://mlsanity.com/paper/2210.02390/) (2022)
- [Prompting for Discovery: Flexible Sense-Making for AI Art-Making with Dreamsheets](https://mlsanity.com/paper/2310.09985/) (2023)
- [GenNI: Human-AI Collaboration for Data-Backed Text Generation](https://mlsanity.com/paper/2110.10185/) (2021)
- [Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges](https://mlsanity.com/paper/2310.11252/) (2023)
- [A Novel Model for Capturing the Multiple Representations during Team Problem Solving based on Verbal Discussions](https://mlsanity.com/paper/2308.06273/) (2023)
- [A Novel Representation to Improve Team Problem Solving in Real-Time](https://mlsanity.com/paper/2310.19539/) (2023)

## Abstract (arXiv)

Creating written products is essential to modern life, including writings about one's identity and personal experiences. However, writing is often a difficult activity that requires extensive effort to frame the central ideas, the pursued approach to communicate the central ideas, e.g., using analogies, metaphors, or other possible means, the needed presentation structure, and the actual verbal expression. Large Language Models, a recently emerged approach in Machine Learning, can offer a significant help in reducing the effort and improving the quality of written products. This paper proposes a new computational approach to explore prompts that given as inputs to a Large Language Models can generate cues to improve the considered written products. Two case studies on improving write-ups, one based on an analogy and one on a metaphor, are also presented in the paper.