taskGen at TSAR 2025 Shared Task Exploring prompt strategies with linguistic knowledge

Juan Cruz Oviedo; Elisabet Comelles Pujadas; Laura Alonso Alemany; Jordi Atserias

taskGen at TSAR 2025 Shared Task Exploring prompt strategies with linguistic knowledge

Juan Cruz Oviedo, Elisabet Comelles Pujadas, Laura Alonso Alemany, Jordi Atserias Batalla

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Abstract

TaskGen ranked as 6th best team in the TSAR 2025 shared task for English text adaptation to a target CEFR level. Our experiments consisted of prompting a Llama-3.1-8B-Instruct model with linguistic descriptors of the target level, examples of adaptations and multi-step approaches. Our best run, 13th in the overall ranking, applied an ensemble strategy using a voting mechanism to find the most adequate among 10 texts, each produced by a different prompting strategy.

Anthology ID:: 2025.tsar-1.13
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
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 160–172
Language:
URL:: https://aclanthology.org/2025.tsar-1.13/
DOI:
Bibkey:
Cite (ACL):: Juan Cruz Oviedo, Elisabet Comelles Pujadas, Laura Alonso Alemany, and Jordi Atserias Batalla. 2025. taskGen at TSAR 2025 Shared Task Exploring prompt strategies with linguistic knowledge. In Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025), pages 160–172, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):: taskGen at TSAR 2025 Shared Task Exploring prompt strategies with linguistic knowledge (Oviedo et al., TSAR 2025)
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PDF:: https://aclanthology.org/2025.tsar-1.13.pdf

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@inproceedings{oviedo-etal-2025-taskgen,
    title = "task{G}en at {TSAR} 2025 Shared Task Exploring prompt strategies with linguistic knowledge",
    author = "Oviedo, Juan Cruz  and
      Comelles Pujadas, Elisabet  and
      Alonso Alemany, Laura  and
      Atserias Batalla, Jordi",
    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-1.13/",
    pages = "160--172",
    ISBN = "979-8-89176-176-6",
    abstract = "TaskGen ranked as 6th best team in the TSAR 2025 shared task for English text adaptation to a target CEFR level. Our experiments consisted of prompting a Llama-3.1-8B-Instruct model with linguistic descriptors of the target level, examples of adaptations and multi-step approaches. Our best run, 13th in the overall ranking, applied an ensemble strategy using a voting mechanism to find the most adequate among 10 texts, each produced by a different prompting strategy."
}

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%0 Conference Proceedings
%T taskGen at TSAR 2025 Shared Task Exploring prompt strategies with linguistic knowledge
%A Oviedo, Juan Cruz
%A Comelles Pujadas, Elisabet
%A Alonso Alemany, Laura
%A Atserias Batalla, Jordi
%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 oviedo-etal-2025-taskgen
%X TaskGen ranked as 6th best team in the TSAR 2025 shared task for English text adaptation to a target CEFR level. Our experiments consisted of prompting a Llama-3.1-8B-Instruct model with linguistic descriptors of the target level, examples of adaptations and multi-step approaches. Our best run, 13th in the overall ranking, applied an ensemble strategy using a voting mechanism to find the most adequate among 10 texts, each produced by a different prompting strategy.
%U https://aclanthology.org/2025.tsar-1.13/
%P 160-172

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Markdown (Informal)

[taskGen at TSAR 2025 Shared Task Exploring prompt strategies with linguistic knowledge](https://aclanthology.org/2025.tsar-1.13/) (Oviedo et al., TSAR 2025)

taskGen at TSAR 2025 Shared Task Exploring prompt strategies with linguistic knowledge (Oviedo et al., TSAR 2025)

ACL

Juan Cruz Oviedo, Elisabet Comelles Pujadas, Laura Alonso Alemany, and Jordi Atserias Batalla. 2025. taskGen at TSAR 2025 Shared Task Exploring prompt strategies with linguistic knowledge. In Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025), pages 160–172, Suzhou, China. Association for Computational Linguistics.