NativQA: Multilingual Culturally-Aligned Natural Query for LLMs
Authors: Md. Arid Hasan, Maram Hasanain, Fatema Ahmad, Sahinur Rahman Laskar, Sunaya Upadhyay, Vrunda N Sukhadia, Mucahid Kutlu, Shammur Absar Chowdhury, Firoj Alam


Reviewer paper summaries: The authors developed what they refer to as a culturally-aware question-and-answering dataset in seven languages, consisting of a total of ~75k pairs.
Summary of strengths: The paper is well-organized and uses multiple images and tables.
Summary of weaknesses: Most of the figures and table captions are not informative enough. The paper introduces a new dataset, however, the argument for the need for such datasets in the paper is not convincing. There are previous QA in all the languages considered in this work, and the authors have not made any argument as to why a new dataset is needed or if the existing datasets are not culturally aligned. Creating a new dataset, especially in low-resource language is a worthy path, however, the authors did not make a convincing argument in the submission.
Comments, suggestions and typos: There should be more valid arguments for the need for the dataset. If the authors believe the existing datasets are not culturally aligned, then they need to explicitly address that in the paper. Most of the figures and tables could use a more detailed caption.
Overall assessment scores: 3