Catalan Text Simplification with Large Language Models
Year: 2026
Type: dissertation
Abstract: Automatic text simplification (ATS) is crucial for accessibility, yet it remains underexplored for low-resource languages. This thesis investigates sentence-level ATS for Catalan using large language models (LLMs). A performance analysis of three LLMs is conducted using the iDEM corpus as a benchmark and SARI as the ATS evaluation metric. Diverse prompt engineering techniques are employed to examine their impact on simplification quality. This work introduces a novel reward function that combines SARI with specific penalty terms to achieve better alignment. The reward function is designed for Group Relative Policy Optimization (GRPO) to guide LLMs toward a targeted simplification style. The effectiveness of GRPO with this reward function is demonstrated by post-training IberianLLM-7B-Instruct on the ASSET dataset. Following post-training, the model’s ATS performance improves, while also successfully suppressing negative behaviors. Cross-lingual transfer learning is explored by translating ASSET into Catalan and Spanish and post-training the model on them.
Author: Ayguadé Domingo, Arnau
Language: English
Subfield: Linguistics and Language
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7214133122