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

TL;DR

This paper explores methods to simplify scientific texts by identifying complex terms and rephrasing sentences using large language models, aiming to improve accessibility for non-expert readers.

Contribution

It introduces a new task focusing on simplifying complex sentences in scientific texts and evaluates the use of Gemini and OpenAI language models for this purpose.

Findings

Language models effectively identify complex terms.

Rephrasing improves understandability for non-experts.

The task advances scientific text accessibility.

Abstract

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.