3 citations · 7 across the 10 of their papers we have counts for
12 papers · 1 filter
EDRAC: Benchmarking Arabic Dialect Reading Comprehension
Noor Abo Mokh, Kirill Chirkunov, Teresa Lynn +15
Dialectal Arabic (DA) remains under-resourced compared to Modern Standard Arabic (MSA), particularly for machine reading comprehension (MRC) and question answering (QA). Existing A…
DialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models
Malik H. Altakrori, Nizar Habash, Abed Alhakim Freihat +7
We present DialectalArabicMMLU, a new benchmark for evaluating the performance of large language models (LLMs) across Arabic dialects. While recently developed Arabic and multiling…
GenAI Content Detection Task 1: English and Multilingual Machine-Generated Text Detection: AI vs. Human
Yuxia Wang, Artem Shelmanov, Jonibek Mansurov +23
We present the GenAI Content Detection Task~1 -- a shared task on binary machine generated text detection, conducted as a part of the GenAI workshop at COLING 2025. The task consis…
A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment
Khalid N. Elmadani, Nizar Habash, Hanada Taha-Thomure
This paper introduces the Balanced Arabic Readability Evaluation Corpus (BAREC), a large-scale, fine-grained dataset for Arabic readability assessment. BAREC consists of 69,441 sen…
Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI
Yuxia Wang, Rui Xing, Jonibek Mansurov +23
Prior studies have shown that distinguishing text generated by Large Language Models (LLMs) from human-written one is highly challenging for humans, and often no better than random…
Guidelines for Fine-grained Sentence-level Arabic Readability Annotation
Nizar Habash, Hanada Taha-Thomure, Khalid N. Elmadani +2
This paper presents the annotation guidelines of the Balanced Arabic Readability Evaluation Corpus (BAREC), a large-scale resource for fine-grained sentence-level readability asses…
LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection
Mervat Abassy, Kareem Elozeiri, Alexander Aziz +21
The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written o…
SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection
Yuxia Wang, Jonibek Mansurov, Petar Ivanov +12
We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtask…
From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
Teresa Lynn, Malik H. Altakrori, Samar Mohamed Magdy +11
The rapid evolution of Natural Language Processing (NLP) has favoured major languages such as English, leaving a significant gap for many others due to limited resources. This is e…
ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic
Fajri Koto, Haonan Li, Sara Shatnawi +10
The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models. While state-of-the-art…
M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection
Yuxia Wang, Jonibek Mansurov, Petar Ivanov +11
The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels. This raises legitimate concerns about its pot…
Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study
Injy Hamed, Nizar Habash, Ngoc Thang Vu
Code-switching (CSW) text generation has been receiving increasing attention as a solution to address data scarcity. In light of this growing interest, we need more comprehensive s…