Efficient On-Device Text Simplification for Firefox with Synthetic Data Fine-Tuning

Pablo Romero, Zihao Li, Matthew Shardlow


Abstract
This work presents a system for on-device text simplification that enables users to process sensitive text without relying on cloud-based services. Through the use of quantization techniques and a novel approach to controllable text simplification we reduce model size by up to 75 percent with minimal performance degradation. Our models demonstrate efficient state-of-the-art results using a synthetic dataset of 2909 examples outperforming prior work trained on 300K examples. This efficiency stems from (1) a single control token strategy that precisely targets specific reading levels (2) a contrastive training approach that enriches model understanding through exposure to multiple simplification levels and (3) individual models that dedicate full parameter capacity to specific reading level transformations. Our best models achieve up to 82.18 BLEU at the Advanced level and 46.12 SARI at the Elementary level on standard benchmarks with performance preserved even after aggressive quantization. This work is implemented as a collaboration with the Mozilla AI team to process text entirely locally ensuring sensitive information never leaves the users device. We have a demonstration video https//youtu.be/TzmaxnARMzg and a web demo available at https//pablorom2004.github.io/Simplification-Web-Demo
Anthology ID:
2025.tsar-1.7
Volume:
Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025)
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Matthew Shardlow, Fernando Alva-Manchego, Kai North, Regina Stodden, Horacio Saggion, Nouran Khallaf, Akio Hayakawa
Venues:
TSAR | WS
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Publisher:
Association for Computational Linguistics
Note:
Pages:
105–115
Language:
URL:
https://preview.aclanthology.org/ingest-emnlp/2025.tsar-1.7/
DOI:
Bibkey:
Cite (ACL):
Pablo Romero, Zihao Li, and Matthew Shardlow. 2025. Efficient On-Device Text Simplification for Firefox with Synthetic Data Fine-Tuning. In Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025), pages 105–115, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Efficient On-Device Text Simplification for Firefox with Synthetic Data Fine-Tuning (Romero et al., TSAR 2025)
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PDF:
https://preview.aclanthology.org/ingest-emnlp/2025.tsar-1.7.pdf