# Generics in science communication: Misaligned interpretations across laypeople, scientists, and large language models

arXiv: https://arxiv.org/abs/2602.06190 · HTML: https://mlsanity.com/paper/2602.06190/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 76 references that are in the map.
- First posted on arXiv 5 Feb 2026.

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

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)
- [An Empirical Study of the Non-determinism of ChatGPT in Code Generation](https://mlsanity.com/paper/2308.02828/) (2023)
- [Mapping the Increasing Use of LLMs in Scientific Papers](https://mlsanity.com/paper/2404.01268/) (2024)
- [Generalization Bias in Large Language Model Summarization of Scientific Research](https://mlsanity.com/paper/2504.00025/) (2025)
- [The strain on scientific publishing](https://mlsanity.com/paper/2309.15884/) (2023)
- [Can ChatGPT evaluate research quality?](https://mlsanity.com/paper/2402.05519/) (2024)
- [Comparative Analysis Based on DeepSeek, ChatGPT, and Google Gemini: Features, Techniques, Performance, Future Prospects](https://mlsanity.com/paper/2503.04783/) (2025)
- [Qualitative Insights Tool (QualIT): LLM Enhanced Topic Modeling](https://mlsanity.com/paper/2409.15626/) (2024)

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

- [How broad is that claim? Mapping Generalisation in NLP Research](https://mlsanity.com/paper/2609.14770/) (2026)

## Abstract (arXiv)

Scientists often use generics, that is, unquantified statements about whole categories of people or phenomena, when communicating research findings (e.g., "statins reduce cardiovascular events"). Large language models (LLMs), such as ChatGPT, frequently adopt the same style when summarizing scientific texts. However, generics can prompt overgeneralizations, especially when they are interpreted differently across audiences. In a study comparing laypeople, scientists, and two leading LLMs (ChatGPT-5 and DeepSeek), we found systematic differences in interpretation of generics. Compared to most scientists, laypeople judged scientific generics as more generalizable and credible, while LLMs rated them even higher. These mismatches highlight significant risks for science communication. Scientists may use generics and incorrectly assume laypeople share their interpretation, while LLMs may systematically overgeneralize scientific findings when summarizing research. Our findings underscore the need for greater attention to language choices in both human and LLM-mediated science communication.