Summary of Nativqa: Multilingual Culturally-aligned Natural Query For Llms, by Md. Arid Hasan et al.
NativQA: Multilingual Culturally-Aligned Natural Query for LLMs
by Md. Arid Hasan, Maram Hasanain, Fatema Ahmad, Sahinur Rahman Laskar, Sunaya Upadhyay, Vrunda N Sukhadia, Mucahid Kutlu, Shammur Absar Chowdhury, Firoj Alam
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a framework, NativQA, to create region-specific natural question answering (QA) datasets in native languages. The goal is to bridge the gap between large language models (LLMs) and real-world applications by developing culturally and regionally aligned QA datasets. The authors demonstrate the efficacy of their approach by creating a multilingual QA dataset, MultiNativQA, comprising ~64k manually annotated QA pairs in seven languages. This dataset is designed for LLM evaluation and fine-tuning, focusing on queries from native speakers from 9 regions covering 18 topics. The paper also benchmarks open- and closed-source LLMs with the MultiNativQA dataset, highlighting the framework’s effectiveness in constructing fine-tuning data for low-resource and dialectally-rich languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve how computers understand questions from people around the world. Right now, there are many language datasets that help test how good computer models are at answering questions. But most of these datasets are not specific to certain regions or cultures. This makes it hard for computer models to learn how to answer questions in different languages and cultures. The authors created a new framework called NativQA that helps make region-specific QA datasets in native languages. They tested this framework by creating a dataset with ~64k questions and answers in 7 languages. This can help improve how well computer models can understand questions from people all over the world. |
Keywords
» Artificial intelligence » Fine tuning » Question answering
