# Know Your Audience: The benefits and pitfalls of generating plain language summaries beyond the "general" audience

arXiv: https://arxiv.org/abs/2403.04979 · HTML: https://mlsanity.com/paper/2403.04979/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2024.

## Built on by (most cited first, 13 of 13)

- [PersonaFlow: Designing LLM-Simulated Expert Perspectives for Enhanced Research Ideation](https://mlsanity.com/paper/2409.12538/) (2024)
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- [ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations](https://mlsanity.com/paper/2506.14200/) (2025)
- [JRE-L: Journalist, Reader, and Editor LLMs in the Loop for Science Journalism for the General Audience](https://mlsanity.com/paper/2501.16865/) (2025)
- [Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL](https://mlsanity.com/paper/2509.01058/) (2025)
- [LLM-Collaboration on Automatic Science Journalism for the General Audience](https://mlsanity.com/paper/2407.09756/) (2024)
- [Readability Reconsidered: A Cross-Dataset Analysis of Reference-Free Metrics](https://mlsanity.com/paper/2510.15345/) (2025)
- [Do We Need Bigger Models for Science? Task-Aware Retrieval with Small Language Models](https://mlsanity.com/paper/2604.01965/) (2026)
- [Breaking the Curse of Knowledge: Designing Personalized Jargon Support for Real-Time Online Meetings](https://mlsanity.com/paper/2508.10239/) (2025)
- [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)
- [CWF: A Collaborative Writing Framework for Personalized and Reliable Popular Science Writing](https://mlsanity.com/paper/2609.06126/) (2026)

## Abstract (arXiv)

Language models (LMs) show promise as tools for communicating science to the general public by simplifying and summarizing complex language. Because models can be prompted to generate text for a specific audience (e.g., college-educated adults), LMs might be used to create multiple versions of plain language summaries for people with different familiarities of scientific topics. However, it is not clear what the benefits and pitfalls of adaptive plain language are. When is simplifying necessary, what are the costs in doing so, and do these costs differ for readers with different background knowledge? Through three within-subjects studies in which we surface summaries for different envisioned audiences to participants of different backgrounds, we found that while simpler text led to the best reading experience for readers with little to no familiarity in a topic, high familiarity readers tended to ignore certain details in overly plain summaries (e.g., study limitations). Our work provides methods and guidance on ways of adapting plain language summaries beyond the single "general" audience.