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20212025
most citedFrom Words to Proverbs: Evaluating LLMs Linguistic and Cultural Competence in Saudi Dialects with Absher

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CL20251 cited

From Words to Proverbs: Evaluating LLMs Linguistic and Cultural Competence in Saudi Dialects with Absher

Renad Al-Monef, Hassan Alhuzali, Nora Alturayeif +1

As large language models (LLMs) become increasingly central to Arabic NLP applications, evaluating their understanding of regional dialects and cultural nuances is essential, parti…

cs.CL2025

SaudiCulture: A Benchmark for Evaluating Large Language Models Cultural Competence within Saudi Arabia

Lama Ayash, Hassan Alhuzali, Ashwag Alasmari +1

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing; however, they often struggle to accurately capture and reflect cultural nuanc…

cs.CL2024

Evaluating the Effectiveness of the Foundational Models for Q&A Classification in Mental Health care

Hassan Alhuzali, Ashwag Alasmari

Pre-trained Language Models (PLMs) have the potential to transform mental health support by providing accessible and culturally sensitive resources. However, despite this potential…

cs.CL2024

Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models

Abdurahmman Alzahrani, Eyad Babkier, Faisal Yanbaawi +2

In the current era of digital communication and widespread use of social media, it is crucial to develop an understanding of persuasive techniques employed in written text. This kn…

cs.CL2024

MentalQA: An Annotated Arabic Corpus for Questions and Answers of Mental Healthcare

Hassan Alhuzali, Ashwag Alasmari, Hamad Alsaleh

Mental health disorders significantly impact people globally, regardless of background, education, or socioeconomic status. However, access to adequate care remains a challenge, pa…

cs.CL2021

SpanEmo: Casting Multi-label Emotion Classification as Span-prediction

Hassan Alhuzali, Sophia Ananiadou

Emotion recognition (ER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications from health and well-being to author profiling…