# THM@SimpleText 2025 -- Task 1.1: Revisiting Text Simplification based on Complex Terms for Non-Experts

arXiv: https://arxiv.org/abs/2507.04414 · HTML: https://mlsanity.com/paper/2507.04414/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 6 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Learning Rich Representation of Keyphrases from Text](https://mlsanity.com/paper/2112.08547/) (2021)
- [Declarative Experimentation in Information Retrieval using PyTerrier](https://mlsanity.com/paper/2007.14271/) (2020)
- [On the Ethical Considerations of Text Simplification](https://mlsanity.com/paper/2204.09565/) (2022)
- [Text Simplification of Scientific Texts for Non-Expert Readers](https://mlsanity.com/paper/2307.03569/) (2023)

## Abstract (arXiv)

Scientific text is complex as it contains technical terms by definition. Simplifying such text for non-domain experts enhances accessibility of innovation and information. Politicians could be enabled to understand new findings on topics on which they intend to pass a law, or family members of seriously ill patients could read about clinical trials. The SimpleText CLEF Lab focuses on exactly this problem of simplification of scientific text. Task 1.1 of the 2025 edition specifically handles the simplification of complex sentences, so very short texts with little context. To tackle this task we investigate the identification of complex terms in sentences which are rephrased using small Gemini and OpenAI large language models for non-expert readers.