Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models

• URL: http://arxiv.org/abs/2402.18397v2
• Date: Thu, 06 Nov 2025 08:34:10 GMT
• Title: Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models
• Authors: Ercong Nie, Shuzhou Yuan, Bolei Ma, Helmut Schmid, Michael Färber, Frauke Kreuter, Hinrich Schütze,
• Abstract summary: We introduce a decomposed prompting approach for sequence labeling tasks.<n>We test our method on the Universal Dependencies part-of-speech tagging dataset for 38 languages.
• Score: 54.58989938395976
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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