# Making Knowledge Accessible: Divergent Readability-Accuracy Strategies of Mistral and QWen in Biomedical Text Simplification

arXiv: https://arxiv.org/abs/2511.05080 · HTML: https://mlsanity.com/paper/2511.05080/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Benchmarking large language models for biomedical natural language processing applications and recommendations](https://mlsanity.com/paper/2305.16326/) (2023)
- [Unveiling the Generalization Power of Fine-Tuned Large Language Models](https://mlsanity.com/paper/2403.09162/) (2024)
- [Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data](https://mlsanity.com/paper/2408.13833/) (2024)
- [A Dataset for Plain Language Adaptation of Biomedical Abstracts](https://mlsanity.com/paper/2210.12242/) (2022)
- [Overview of the BioLaySumm 2024 Shared Task on the Lay Summarization of Biomedical Research Articles](https://mlsanity.com/paper/2408.08566/) (2024)
- [Do LLMs Really Adapt to Domains? An Ontology Learning Perspective](https://mlsanity.com/paper/2407.19998/) (2024)
- [Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries](https://mlsanity.com/paper/2305.10163/) (2023)
- [An In-depth Evaluation of Large Language Models in Sentence Simplification with Error-based Human Assessment](https://mlsanity.com/paper/2403.04963/) (2024)
- [A Critical Look at Meta-evaluating Summarisation Evaluation Metrics](https://mlsanity.com/paper/2409.19507/) (2024)

## Abstract (arXiv)

The growing public demand for accessible biomedical information calls for scalable text simplification. While large language models (LLMs) offer solutions, they too struggle with balancing improved readability against preservation of meaning. This report empirically compares how two LLMs - instruction-tuned Mistral-Small 3 24B and the reasoning-augmented QWen2.5 32B- navigate this trade-off in biomedical text simplification, benchmarked against human performance. Our analysis highlights how each model applies distinct operational strategies when simplifying biomedical text. Mistral exhibits a tempered lexical simplification approach that consistently enhances readability across multiple metrics while preserving discourse fidelity (BERTScore: 0.91, statistically comparable to that of humans). In comparison, QWen also attains enhanced readability performance and a reasonable BERTScore of 0.89, but presents a disconnect in balancing between readability and accuracy. Additionally, a comprehensive correlation analysis of a suite of 21 metrics confirms strong functional redundancies in metrics and informs adaptation requirements.