Summary of Exploring Retrieval Augmented Generation in Arabic, by Samhaa R. El-beltagy and Mohamed A. Abdallah
Exploring Retrieval Augmented Generation in Arabic
by Samhaa R. El-Beltagy, Mohamed A. Abdallah
First submitted to arxiv on: 14 Aug 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 The paper presents a comprehensive study on Retrieval Augmented Generation (RAG) for Arabic text, combining strengths of retrieval-based and generation-based models to enhance text generation tasks. It explores various semantic embedding models in the retrieval stage and several Large Language Models (LLMs) in the generation stage, investigating what works and what doesn’t in the context of Arabic. The work also addresses variations between document dialect and query dialect in the retrieval stage. Results demonstrate that existing semantic embedding models and LLMs can be effectively employed to build Arabic RAG pipelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG is a new way to improve text generation tasks by combining two different approaches. Right now, this technique has mostly been used for English, but what about Arabic? This paper tries to figure out how well it works for Arabic and which parts of the process work best. They test different ways of finding related texts (semantic embedding models) and generating new text (Large Language Models). The results show that we can use existing methods to make a RAG system for Arabic. |
Keywords
» Artificial intelligence » Embedding » Rag » Retrieval augmented generation » Text generation
