# Steering AI-Driven Personalization of Scientific Text for General Audiences

arXiv: https://arxiv.org/abs/2411.09969 · HTML: https://mlsanity.com/paper/2411.09969/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 95 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 18 of 18)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021)
- [PersonalLLM: Tailoring LLMs to Individual Preferences](https://mlsanity.com/paper/2409.20296/) (2024)
- [Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols](https://mlsanity.com/paper/2009.14237/) (2020)
- [Beyond the Chat: Executable and Verifiable Text-Editing with LLMs](https://mlsanity.com/paper/2309.15337/) (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)
- [Scim: Intelligent Skimming Support for Scientific Papers](https://mlsanity.com/paper/2205.04561/) (2022)
- [Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing](https://mlsanity.com/paper/2203.00130/) (2022)
- [Fluid Transformers and Creative Analogies: Exploring Large Language Models' Capacity for Augmenting Cross-Domain Analogical Creativity](https://mlsanity.com/paper/2302.12832/) (2023)
- [Patchview: LLM-Powered Worldbuilding with Generative Dust and Magnet Visualization](https://mlsanity.com/paper/2408.04112/) (2024)
- [Refocusing on Relevance: Personalization in NLG](https://mlsanity.com/paper/2109.05140/) (2021)
- [Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and Labor](https://mlsanity.com/paper/2305.10374/) (2023)
- [Authors' Values and Attitudes Towards AI-bridged Scalable Personalization of Creative Language Arts](https://mlsanity.com/paper/2403.00439/) (2024)
- [Know Your Audience: The benefits and pitfalls of generating plain language summaries beyond the "general" audience](https://mlsanity.com/paper/2403.04979/) (2024)
- [An AI-Resilient Text Rendering Technique for Reading and Skimming Documents](https://mlsanity.com/paper/2401.10873/) (2024)
- [OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangers](https://mlsanity.com/paper/2201.11709/) (2022)
- [Talk to Papers: Bringing Neural Question Answering to Academic Search](https://mlsanity.com/paper/2004.02002/) (2020)
- [The Unwanted Dissemination of Science: The Usage of Academic Articles as Ammunition in Contested Discursive Arenas on Twitter](https://mlsanity.com/paper/2402.17495/) (2024)

## Built on by (most cited first, 7 of 7)

- [Transparent Adaptive Learning via Data-Centric Multimodal Explainable AI](https://mlsanity.com/paper/2508.00665/) (2025)
- [The Variance Paradox: How AI Reduces Diversity but Increases Novelty](https://mlsanity.com/paper/2508.19264/) (2025)
- [Breaking the Curse of Knowledge: Designing Personalized Jargon Support for Real-Time Online Meetings](https://mlsanity.com/paper/2508.10239/) (2025)
- [ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?](https://mlsanity.com/paper/2605.00468/) (2026)
- [Explain Like I'm 5 or Whatever I Choose: Evaluating the Potential for Interactive Complexity in Language Model Responses](https://mlsanity.com/paper/2606.06788/) (2026)
- [Long-term Measurements: Towards a Longitudinal Understanding of Human-AI Interactions](https://mlsanity.com/paper/2608.02491/) (2026)
- [CWF: A Collaborative Writing Framework for Personalized and Reliable Popular Science Writing](https://mlsanity.com/paper/2609.06126/) (2026)

## Abstract (arXiv)

Digital media platforms (e.g., science blogs) offer opportunities to communicate scientific content to general audiences at scale. However, these audiences vary in their scientific expertise, literacy levels, and personal backgrounds, making effective science communication challenging. To address this challenge, we designed TranSlider, an AI-powered tool that generates personalized translations of scientific text based on individual user profiles (e.g., hobbies, location, and education). Our tool features an interactive slider that allows users to steer the degree of personalization from 0 (weakly relatable) to 100 (strongly relatable), leveraging LLMs to generate the translations with chosen degrees. Through an exploratory study with 15 participants, we investigated both the utility of these AI-personalized translations and how interactive reading features influenced users' understanding and reading experiences. We found that participants who preferred higher degrees of personalization appreciated the relatable and contextual translations, while those who preferred lower degrees valued concise translations with subtle contextualization. Furthermore, participants reported the compounding effect of multiple translations on their understanding of scientific content. Drawing on these findings, we discuss several implications for facilitating science communication and designing steerable interfaces to support human-AI alignment.