GuideQ: Framework for Guided Questioning for progressive informational collection and classification

Priya Mishra, Suraj Racha, Kaustubh Ponkshe, Adit Akarsh, Ganesh Ramakrishnan


Abstract
The veracity of a factoid is largely independent of the language it is written in. However, language models are inconsistent in their ability to answer the same factual question across languages. This raises questions about how LLMs represent a given fact across languages. We explore multilingual factual knowledge through two aspects: the model’s ability to answer a query consistently across languages, and the ability to ”store” answers in a shared representation for several languages. We propose a methodology to measure the extent of representation sharing across languages by repurposing knowledge editing methods. We examine LLMs with various multilingual configurations using a new multilingual dataset. We reveal that high consistency does not necessarily imply shared representation, particularly for languages with different scripts. Moreover, we find that script similarity is a dominant factor in representation sharing. Finally, we observe that if LLMs could fully share knowledge across languages, their accuracy in their best-performing language could benefit an increase of up to 150% on average. These findings highlight the need for improved multilingual knowledge representation in LLMs and suggest a path for the development of more robust and consistent multilingual LLMs.
Anthology ID:
2025.findings-naacl.261
Volume:
Findings of the Association for Computational Linguistics: NAACL 2025
Month:
April
Year:
2025
Address:
Albuquerque, New Mexico
Editors:
Luis Chiruzzo, Alan Ritter, Lu Wang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4630–4644
Language:
URL:
https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.261/
DOI:
Bibkey:
Cite (ACL):
Priya Mishra, Suraj Racha, Kaustubh Ponkshe, Adit Akarsh, and Ganesh Ramakrishnan. 2025. GuideQ: Framework for Guided Questioning for progressive informational collection and classification. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 4630–4644, Albuquerque, New Mexico. Association for Computational Linguistics.
Cite (Informal):
GuideQ: Framework for Guided Questioning for progressive informational collection and classification (Mishra et al., Findings 2025)
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PDF:
https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.261.pdf