@inproceedings{shimada-etal-2025-hit,
title = "{HIT}-{YOU} at {TSAR} 2025 Shared Task Leveraging Similarity-Based Few-Shot Prompting, Round-Trip Translation, and Self-Refinement for Readability-Controlled Text Simplification",
author = "Shimada, Mao and
Bian, Kexin and
Ling, Zhidong and
Komachi, Mamoru",
editor = "Shardlow, Matthew and
Alva-Manchego, Fernando and
North, Kai and
Stodden, Regina and
Saggion, Horacio and
Khallaf, Nouran and
Hayakawa, Akio",
booktitle = "Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025)",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.tsar-1.20/",
pages = "231--241",
ISBN = "979-8-89176-176-6",
abstract = "We describe our submission to the TSAR 2025 shared task on readability-controlled text simplification, which evaluates systems on their ability to adjust linguistic complexity to specified CEFR levels while preserving meaning and coherence. We explored two complementary frameworks leveraging the shared task CEFR classifier as feedback. The first is an ensemble approach generating diverse candidates using multiple LLMs under zero-shot prompting with level-specific instructions and vocabulary lists, one-shot prompting, and round-trip translation. Candidates were filtered by predicted CEFR level before an LLM judge selected the final output. The second framework is a self-refinement loop, where a single candidate is iteratively revised with classifier feedback until matching the target level or reaching a maximum number of iterations. This study is among the first to apply round-trip translation and iterative self-refinement to controlled simplification, broadening the toolkit for adapting linguistic complexity."
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%0 Conference Proceedings
%T HIT-YOU at TSAR 2025 Shared Task Leveraging Similarity-Based Few-Shot Prompting, Round-Trip Translation, and Self-Refinement for Readability-Controlled Text Simplification
%A Shimada, Mao
%A Bian, Kexin
%A Ling, Zhidong
%A Komachi, Mamoru
%Y Shardlow, Matthew
%Y Alva-Manchego, Fernando
%Y North, Kai
%Y Stodden, Regina
%Y Saggion, Horacio
%Y Khallaf, Nouran
%Y Hayakawa, Akio
%S Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-176-6
%F shimada-etal-2025-hit
%X We describe our submission to the TSAR 2025 shared task on readability-controlled text simplification, which evaluates systems on their ability to adjust linguistic complexity to specified CEFR levels while preserving meaning and coherence. We explored two complementary frameworks leveraging the shared task CEFR classifier as feedback. The first is an ensemble approach generating diverse candidates using multiple LLMs under zero-shot prompting with level-specific instructions and vocabulary lists, one-shot prompting, and round-trip translation. Candidates were filtered by predicted CEFR level before an LLM judge selected the final output. The second framework is a self-refinement loop, where a single candidate is iteratively revised with classifier feedback until matching the target level or reaching a maximum number of iterations. This study is among the first to apply round-trip translation and iterative self-refinement to controlled simplification, broadening the toolkit for adapting linguistic complexity.
%U https://aclanthology.org/2025.tsar-1.20/
%P 231-241
Markdown (Informal)
[HIT-YOU at TSAR 2025 Shared Task Leveraging Similarity-Based Few-Shot Prompting, Round-Trip Translation, and Self-Refinement for Readability-Controlled Text Simplification](https://aclanthology.org/2025.tsar-1.20/) (Shimada et al., TSAR 2025)
ACL