Summary of Cameleval: Advancing Culturally Aligned Arabic Language Models and Benchmarks, by Zhaozhi Qian and Faroq Altam and Muhammad Alqurishi and Riad Souissi
CamelEval: Advancing Culturally Aligned Arabic Language Models and Benchmarks
by Zhaozhi Qian, Faroq Altam, Muhammad Alqurishi, Riad Souissi
First submitted to arxiv on: 19 Sep 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 introduces Juhaina, a Large Language Model (LLM) specifically designed for Arabic speakers. It is an bilingual LLM that supports advanced functionalities such as instruction following, open-ended question answering, and information provisioning. The model contains 9.24 billion parameters and is trained on a context window of up to 8,192 tokens. Juhaina surpasses existing LLMs like the Llama and Gemma families in generating helpful responses in Arabic, providing factually accurate information about the region, and understanding nuanced cultural aspects. The paper also proposes a new evaluation benchmark, CamelEval, to assess the performance of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Juhaina is a special kind of computer program that can understand and respond to human language. This program is designed for people who speak Arabic and want to use it to communicate with others or get information. The creators of Juhaina made it very smart by training it on lots of text data, which means it can do things like answer questions, provide information, and even understand cultural references. They also compared Juhaina to other similar programs and found that it does a better job in many areas, especially when communicating in Arabic. |
Keywords
» Artificial intelligence » Context window » Large language model » Llama » Question answering
