@inproceedings{nie-etal-2025-decomposed,
title = "Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models",
author = {Nie, Ercong and
Yuan, Shuzhou and
Ma, Bolei and
Schmid, Helmut and
F{\"a}rber, Michael and
Kreuter, Frauke and
Schuetze, Hinrich},
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-ijcnlp.38/",
pages = "646--659",
ISBN = "979-8-89176-303-6",
abstract = "Probing the multilingual knowledge of linguistic structure in LLMs, often characterized as sequence labeling, faces challenges with maintaining output templates in current text-to-text prompting strategies. To solve this, we introduce a decomposed prompting approach for sequence labeling tasks. Diverging from the single text-to-text prompt, our prompt method generates for each token of the input sentence an individual prompt which asks for its linguistic label. We test our method on the Universal Dependencies part-of-speech tagging dataset for 38 languages, using both English-centric and multilingual LLMs. Our findings show that decomposed prompting surpasses the iterative prompting baseline in efficacy and efficiency under zero- and few-shot settings. Moreover, our analysis of multilingual performance of English-centric LLMs yields insights into the transferability of linguistic knowledge via multilingual prompting."
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%0 Conference Proceedings
%T Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models
%A Nie, Ercong
%A Yuan, Shuzhou
%A Ma, Bolei
%A Schmid, Helmut
%A Färber, Michael
%A Kreuter, Frauke
%A Schuetze, Hinrich
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F nie-etal-2025-decomposed
%X Probing the multilingual knowledge of linguistic structure in LLMs, often characterized as sequence labeling, faces challenges with maintaining output templates in current text-to-text prompting strategies. To solve this, we introduce a decomposed prompting approach for sequence labeling tasks. Diverging from the single text-to-text prompt, our prompt method generates for each token of the input sentence an individual prompt which asks for its linguistic label. We test our method on the Universal Dependencies part-of-speech tagging dataset for 38 languages, using both English-centric and multilingual LLMs. Our findings show that decomposed prompting surpasses the iterative prompting baseline in efficacy and efficiency under zero- and few-shot settings. Moreover, our analysis of multilingual performance of English-centric LLMs yields insights into the transferability of linguistic knowledge via multilingual prompting.
%U https://aclanthology.org/2025.findings-ijcnlp.38/
%P 646-659
Markdown (Informal)
[Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models](https://aclanthology.org/2025.findings-ijcnlp.38/) (Nie et al., Findings 2025)
ACL
- Ercong Nie, Shuzhou Yuan, Bolei Ma, Helmut Schmid, Michael Färber, Frauke Kreuter, and Hinrich Schuetze. 2025. Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 646–659, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.