arXiv AI

Hadith computational science in the age of large language models: a critical narrative review

The article reviews how transformer models, retrieval‑grounded pipelines, and large language models are reshaping hadith computational science. It critically evaluates existing literature, highlighting uneven progress: expanded data resources and mature segmentation tasks, yet persistent issues such as narrow corpora, weak benchmark comparability, and limited reproducibility. The authors argue that hadith computation should be viewed as an evidence‑infrastructure problem requiring knowledge integration, provenance, and expert supervision, and they propose a research agenda to strengthen the field’s methodological rigor.

arXiv AI

Aug 24

Ansari: A Retrieval-Grounded Islamic AI Assistant -- Architecture, Deployment, and Lessons from 140,000 Conversations

Ansari is a retrieval‑grounded Islamic AI assistant that has handled over 140,000 conversations in more than 25 languages since June 2023. It uses an agentic retrieval loop where a language model searches authenticated Islamic corpora—including the Qur’an, hadith collections, fiqh encyclopedias, and tafsir sources—and answers only based on retrieved content, providing citations for verification. The paper details Ansari’s architecture, multi‑platform deployment, evaluation results (including top performance on the IslamicMMLU leaderboard and strong resistance to false premises), and lessons for faith‑sensitive LLM deployments.

By M Waleed Kadous, Amr Elsayed, Abdullah Al Nahas, Ashraf Haress

llms agents benchmarks

arXiv AI

Jul 28

Grading the Narrators: An Isnad-Rijal Framework for Claim-Level Provenance in Multi-Agent Knowledge Systems

arXiv:2607. 24117v1 Announce Type: new Abstract: Modern multi-agent knowledge systems increasingly accumulate knowledge through chains of autonomous transformations rather than direct retrieval.

By Ali Zahid Raja

agents

Hugging Face Trending Papers

Jul 22

HalluTruthQA: A Fine-Grained Benchmark for Hallucination Detection, Localization, and Explanation in Arabic Question Answering

Large language models (LLMs) can generate fluent Arabic answers, yet factual errors remain difficult to detect, localize, explain, and verify. Existing hallucination benchmarks often provide response-level labels, with limited support for identifying the exact erroneous content, explaining why it is incorrect, or selecting the correct factual answer.

llms nlp benchmarks safety

arXiv Computation and Language

Sep 1

Extending AI for Research to the Humanities: A Multi-Agent Framework for Evidence-Grounded Scholarship

arXiv:2605.30947v4 Announce Type: replace Abstract: LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and qu...

By Yating Pan, Jiajun Zhang, Jun Wang, Qi Su

llms rag agents benchmarks

arXiv AI

Jul 8

Integrating knowledge graphs and multilingual scholarly corpora for domain-adaptive LLMs in SSH

arXiv:2607. 05956v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into scientific research workflows, particularly for bibliographic discovery and literature synthesis, raises significant methodological, epistemic and regulatory challenges for the Social Sciences and Humanities (SSH), especially with regard to disciplinary diversity, multilingual access to sources and the evaluation of results.

By Adam Faci, Alessio Miaschi, Anne Combe, Pascal Cuxac, Francesca Frontini, Nicolas Larrousse, St\'ephane Pouyllau

llms rag nlp benchmarks safety

Hugging Face Trending Papers

Jul 7

Integrating knowledge graphs and multilingual scholarly corpora for domain-adaptive LLMs in SSH

The integration of Large Language Models (LLMs) into scientific research workflows, particularly for bibliographic discovery and literature synthesis, raises significant methodological, epistemic and regulatory challenges for the Social Sciences and Humanities (SSH), especially with regard to disciplinary diversity, multilingual access to sources and the evaluation of results. This paper presents an on-going use case developed within the European project LLMs4EU and the ALT-EDIC infrastructure, aimed at adapting foundation models to SSH research practices and supporting tasks such as question answering, comparative document analysis and literature review.

llms rag nlp benchmarks safety

arXiv AI

Aug 13

JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesis

arXiv:2608. 11741v1 Announce Type: cross Abstract: The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context.

By Ran Li, Huiguo He, Jiahuan Cao, Junle Liu, Hiuyi Cheng, Lianwen Jin

llms nlp fine-tuning multimodal benchmarks

arXiv Computation and Language

Sep 15

A Corpus-Aligned Uthmani-to-Standard Quranic Word Mapping and a Deterministic Recitation Validator

arXiv:2609.14967v1 Announce Type: new Abstract: Quranic text is distributed in two orthographic forms that are byte-level distinct: the Uthmani script used in every printed mushaf, and the Standard (...

By Yahya Mohamed Elnawasany

llms nlp multimodal

arXiv Computation and Language

Aug 25

A Comprehensive Analysis of Arabic Natural Language Processing Research: Trends, Topic Evolution, and Research Gaps -- A Bibliometric and Topic-Based Study

arXiv:2608.23421v1 Announce Type: new Abstract: Natural Language Processing (NLP) has grown rapidly over the past decade, driven by digital transformation in the Arab world, social media, and large l...

By Mullosharaf K. Arabov

llms nlp benchmarks

arXiv AI

Sep 15

Mizan: A National Benchmark for Evaluating Large Language Models on Iraqi Arabic and the Iraqi Civic Context

arXiv:2609.13980v1 Announce Type: cross Abstract: Arabic large-language-model (LLM) evaluation has matured around Modern Standard Arabic (MSA): aggregated leaderboards such as the Open Arabic LLM Lea...

By Nawar S. Alseelawi, Mustafa S. Aljumaily

llms benchmarks safety

arXiv Computation and Language

Sep 21

QuranicMMLU: A Cognitively-Aware Benchmark for Evaluating Generative AI Solutions on Quranic Linguistic Knowledge

arXiv:2609.22038v1 Announce Type: new Abstract: We introduce QuranicMMLU, a benchmark for evaluating generative AI on Quranic Arabic across multiple dimensions of linguistic complexity. Existing Qura...

By Rawan El Ghali, Umm Kulsoom, Anas Madkoor, Dima Faris Alsaudi, Roaa Abdelmagid, Roaa Ibrahim, Raghad Mousa, Hamza Aljaji, Abdullah Khanafer, Abdallah Alkanani, Salah Feras Alali, Rawan Khaled Mohamed, Ehsaneddin Asgari

llms nlp benchmarks

Hugging Face Trending Papers

Aug 12

JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesis

The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context. However, existing computational approaches focus narrowly on subtasks such as character recognition and retrieval, lacking the structured datasets and benchmarks required for comprehensive scholarly analysis.

llms nlp fine-tuning multimodal benchmarks

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