# LLM assisted writing deserves empirical evaluation

arXiv: https://arxiv.org/abs/2608.22124 · HTML: https://mlsanity.com/paper/2608.22124/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 22 Aug 2026.

## Builds on (most cited first, 3 of 3)

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Delving into LLM-assisted writing in biomedical publications through excess vocabulary](https://mlsanity.com/paper/2406.07016/) (2024)
- [Friend or Foe? Exploring the Implications of Large Language Models on the Science System](https://mlsanity.com/paper/2306.09928/) (2023)

## Abstract (arXiv)

LLM-assisted writing is often treated as a detection problem, as it raises questions about clarity, integrity, equity, and evaluation. An analysis of 69,209 Health Informatics papers links it to more focused presentation, broader citation practices, and more globally distributed authorship. These patterns do not prove better science, but they support evaluating manuscripts by scholarly quality and accountability rather than by tool use.