Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models Article Swipe
Ercong Nie
,
Shuzhou Yuan
,
Bolei Ma
,
Helmut Schmid
,
Michael Färber
,
Frauke Kreuter
,
Hinrich Schütze
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.18397
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.18397
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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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2402.18397
- https://arxiv.org/pdf/2402.18397
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4392340991Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2402.18397Digital Object Identifier
- Title
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Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language ModelsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-28Full publication date if available
- Authors
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Ercong Nie, Shuzhou Yuan, Bolei Ma, Helmut Schmid, Michael Färber, Frauke Kreuter, Hinrich SchützeList of authors in order
- Landing page
-
https://arxiv.org/abs/2402.18397Publisher landing page
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https://arxiv.org/pdf/2402.18397Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2402.18397Direct OA link when available
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Linguistics, Computer science, Natural language processing, Psychology, Artificial intelligence, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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