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20232026
most citedChatGPT for Arabic Grammatical Error Correction

4 citations · 5 across the 12 of their papers we have counts for

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14 papers · 1 filter

cs.CL2026

ChronoLens: Measuring Language Change Across Time, Languages, and Linguistic Levels

Gagan Bhatia, Julian Schlenker, Simone Paolo Ponzetto +1

Historical language change affects morphology, syntax, semantics, and pragmatics, yet computational studies typically examine these levels with incompatible representations and the…

cs.CL2026

Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025

Maria Kunilovskaya, Gagan Bhatia, Lisa Sophie Albertelli +10

Human annotation is the empirical foundation of much NLP research, from dataset construction to model evaluation, but papers often leave unclear who produced the annotations and ho…

cs.CL2026

Beyond LLM-as-a-Judge: Deterministic Metrics for Multilingual Generative Text Evaluation

Firoj Alam, Gagan Bhatia, Sahinur Rahman Laskar +1

While Large Language Models (LLMs) are increasingly adopted as automated judges for evaluating generated text, their outputs are often costly, and highly sensitive to prompt design…

cs.CL2026

What Really Controls Temporal Reasoning in Large Language Models: Tokenisation or Representation of Time?

Gagan Bhatia, Ahmad Muhammad Isa, Maxime Peyrard +1

We present MultiTempBench, a multilingual temporal reasoning benchmark spanning three tasks, date arithmetic, time zone conversion, and temporal relation extraction across five lan…

cs.CL2026

Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA

Ummar Abbas, Mourad Ouzzani, Mohamed Y. Eltabakh +7

Large language models (LLMs) can answer religious knowledge queries fluently, yet they often hallucinate and misattribute sources, which is especially consequential in Islamic sett…

cs.CL2026

From RAG to Agentic RAG for Faithful Islamic Question Answering

Gagan Bhatia, Hamdy Mubarak, Mustafa Jarrar +8

Large Language Models (LLMs) are increasingly used for Islamic question answering, where ungrounded responses may carry serious religious consequences. Yet standard MCQ/MRC-style e…