Summary of Persianrag: a Retrieval-augmented Generation System For Persian Language, by Hossein Hosseini et al.
PersianRAG: A Retrieval-Augmented Generation System for Persian Language
by Hossein Hosseini, Mohammad Sobhan Zare, Amir Hossein Mohammadi, Arefeh Kazemi, Zahra Zojaji, Mohammad Ali Nematbakhsh
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 The proposed PersianRAG model, which combines large-scale generative models with external retrieval mechanisms, has shown significant success in various natural language processing tasks. The integration of these components is particularly challenging when applied to low-resource languages like Persian. To address this challenge, the authors propose novel solutions for preprocessing, embedding, retrieval, prompt construction, language modeling, and response evaluation. The proposed framework is evaluated on several Persian benchmark datasets, demonstrating its ability to enhance question answering capabilities in Persian. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new model called PersianRAG that helps answer questions in the Persian language. This is important because Persian is a low-resource language, meaning there isn’t as much information available about it compared to other languages. The authors came up with solutions for some big problems they found when trying to make this model work, like getting the text ready for use and figuring out what words mean. They tested their approach on several sets of Persian texts and showed that it improves how well a computer can answer questions in Persian. |
Keywords
» Artificial intelligence » Embedding » Natural language processing » Prompt » Question answering
