@inproceedings{gao-etal-2026-evaluating,
title = "Evaluating Direct Preference Optimization for Personalizing {G}erman Automatic Text Simplifications for Persons with Intellectual Disabilities",
author = {Gao, Yingqiang and
Johnson, Kaede and
Fr{\"o}hlich, David and
Carrer, Luisa and
Ebling, Sarah},
editor = "Chakravarthi, Bharathi Raja and
B, Bharathi and
Buitelaar, Paul and
Thenmozhi, Durairaj and
Garc{\'i}a Cumbreras, Miguel {\'A}ngel and
Jim{\'e}nez Zafra, Salud Mar{\'i}a",
booktitle = "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = jul,
year = "2026",
address = "Virtual (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.ltedi-1.5/",
pages = "43--62",
ISBN = "979-8-89176-424-8",
abstract = "Automatic text simplification (ATS) aims to enhance language accessibility for various target groups, particularly persons with intellectual disabilities. Recent advancements in large language models (LLMs) have substantially improved the quality of machine-generated text simplifications, however, existing LLM-based ATS systems do not incorporate preference feedback during post-training, resulting in a lack of personalization tailored to the specific needs of target group persons. In this work, we propose an ATS personalization framework using direct preference optimization (DPO). Specifically, we post-trained LLM-based ATS models using human feedback collected from persons with intellectual disabilities, reflecting their preferences of paired text simplifications generated by mainstream LLMs. Our pipeline for developing personalized LLM-based ATS systems encompasses data collection, model selection, supervised fine-tuning (SFT) and DPO post-training, and result evaluation. Our findings underscore the necessity of active participation of target group persons in designing personalized inclusive AI solutions aligned with human preferences."
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<abstract>Automatic text simplification (ATS) aims to enhance language accessibility for various target groups, particularly persons with intellectual disabilities. Recent advancements in large language models (LLMs) have substantially improved the quality of machine-generated text simplifications, however, existing LLM-based ATS systems do not incorporate preference feedback during post-training, resulting in a lack of personalization tailored to the specific needs of target group persons. In this work, we propose an ATS personalization framework using direct preference optimization (DPO). Specifically, we post-trained LLM-based ATS models using human feedback collected from persons with intellectual disabilities, reflecting their preferences of paired text simplifications generated by mainstream LLMs. Our pipeline for developing personalized LLM-based ATS systems encompasses data collection, model selection, supervised fine-tuning (SFT) and DPO post-training, and result evaluation. Our findings underscore the necessity of active participation of target group persons in designing personalized inclusive AI solutions aligned with human preferences.</abstract>
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%0 Conference Proceedings
%T Evaluating Direct Preference Optimization for Personalizing German Automatic Text Simplifications for Persons with Intellectual Disabilities
%A Gao, Yingqiang
%A Johnson, Kaede
%A Fröhlich, David
%A Carrer, Luisa
%A Ebling, Sarah
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Thenmozhi, Durairaj
%Y García Cumbreras, Miguel Ángel
%Y Jiménez Zafra, Salud María
%S Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2026
%8 July
%I Association for Computational Linguistics
%C Virtual (Online)
%@ 979-8-89176-424-8
%F gao-etal-2026-evaluating
%X Automatic text simplification (ATS) aims to enhance language accessibility for various target groups, particularly persons with intellectual disabilities. Recent advancements in large language models (LLMs) have substantially improved the quality of machine-generated text simplifications, however, existing LLM-based ATS systems do not incorporate preference feedback during post-training, resulting in a lack of personalization tailored to the specific needs of target group persons. In this work, we propose an ATS personalization framework using direct preference optimization (DPO). Specifically, we post-trained LLM-based ATS models using human feedback collected from persons with intellectual disabilities, reflecting their preferences of paired text simplifications generated by mainstream LLMs. Our pipeline for developing personalized LLM-based ATS systems encompasses data collection, model selection, supervised fine-tuning (SFT) and DPO post-training, and result evaluation. Our findings underscore the necessity of active participation of target group persons in designing personalized inclusive AI solutions aligned with human preferences.
%U https://aclanthology.org/2026.ltedi-1.5/
%P 43-62
Markdown (Informal)
[Evaluating Direct Preference Optimization for Personalizing German Automatic Text Simplifications for Persons with Intellectual Disabilities](https://aclanthology.org/2026.ltedi-1.5/) (Gao et al., LTEDI 2026)
ACL