A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation

• URL: http://arxiv.org/abs/2509.02864v1
• Date: Tue, 02 Sep 2025 22:21:55 GMT
• Title: A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation
• Authors: Kesen Wang, Daulet Toibazar, Pedro J. Moreno,
• Abstract summary: We present an end-to-end, self-evolving adversarial workflow for long-context Question-Answer (QA) Generation in Arabic.<n>Our system iteratively refines its own performance without any human intervention.<n>We release AraLongBench, a large-scale Arabic benchmark of single- and multi-page challenges.
• Score: 4.208390540058878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present an end-to-end, self-evolving adversarial workflow for long-context Question-Answer (QA) Generation in Arabic. By orchestrating multiple specialized LVLMs: a question generator, an evaluator, and a swarm of answer generators, our system iteratively refines its own performance without any human intervention. Starting from raw, multi-page Arabic documents across diverse domains, the question generator produces fine-grained, context-aware queries to be tackled by the answer generator swarm, and the evaluator assesses and feeds back quality metrics. This closed-loop cycle enables continuous learning: low-confidence outputs trigger automated re-generation and model updates, progressively enhancing question difficulty and relevance. Moreover, we set the quality metrics as a tunable hyperparameter, enabling question generation at controllable and customizable difficulty levels. We release AraLongBench, a large-scale Arabic benchmark of single- and multi-page challenges spanning hundreds of pages, and demonstrate that our self-evolving workflow substantially outperform static pipelines, markedly boosting the long-context comprehension capabilities of leading Arabic Large Vision Language Models (LVLMs). Lastly, we also meticulously architect a fully automated agentic workflow for long-context Arabic document collection.

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