LAILA: A Large Trait-Based Dataset for Arabic Automated Essay Scoring

TL;DR

LAILA is the largest publicly available Arabic AES dataset, enabling improved automated scoring by providing comprehensive annotations across multiple writing traits and benchmarking state-of-the-art models.

Contribution

This paper introduces LAILA, the first large-scale Arabic AES dataset with detailed trait annotations, facilitating advanced research and model development.

Findings

State-of-the-art models achieve promising results on LAILA

Cross-prompt performance indicates the dataset's robustness

Trait-specific scoring enhances AES accuracy

Abstract

Automated Essay Scoring (AES) has gained increasing attention in recent years, yet research on Arabic AES remains limited due to the lack of publicly available datasets. To address this, we introduce LAILA, the largest publicly available Arabic AES dataset to date, comprising 7,859 essays annotated with holistic and trait-specific scores on seven dimensions: relevance, organization, vocabulary, style, development, mechanics, and grammar. We detail the dataset design, collection, and annotations, and provide benchmark results using state-of-the-art Arabic and English models in prompt-specific and cross-prompt settings. LAILA fills a critical need in Arabic AES research, supporting the development of robust scoring systems.