Summary of Aradice: Benchmarks For Dialectal and Cultural Capabilities in Llms, by Basel Mousi et al.
AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs
by Basel Mousi, Nadir Durrani, Fatema Ahmad, Md. Arid Hasan, Maram Hasanain, Tameem Kabbani, Fahim Dalvi, Shammur Absar Chowdhury, Firoj Alam
First submitted to arxiv on: 17 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 The paper introduces seven synthetic datasets for dialectal Arabic alongside Modern Standard Arabic, created using machine translation and human post-editing. The authors present AraDiCE, a benchmark for evaluating Arabic dialect and cultural evaluation. They evaluate large language models on dialect comprehension and generation, focusing on low-resource Arabic dialects. Additionally, they introduce the first fine-grained benchmark to evaluate cultural awareness across Gulf, Egypt, and Levant regions. The findings show that Arabic-specific models outperform multilingual models on dialectal tasks, but challenges persist in dialect identification, generation, and translation. The study contributes 45K post-edited samples, a cultural benchmark, and highlights the importance of tailored training for improving LLM performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers understand different kinds of Arabic language better. Right now, most computer models can only understand one kind of Arabic well, but there are many other dialects that they don’t understand very well. To fix this, the authors created seven new datasets of text in these different dialects and a way to evaluate how well computer models can understand them. They also tested some existing computer models on these tasks and found out which ones did best. The study shows that special Arabic models are better than general language models at understanding dialectal Arabic, but there’s still more work to be done to make computers understand all the different kinds of Arabic language. |
Keywords
» Artificial intelligence » Translation
