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20242026
most citedAdvancing Security with Digital Twins: A Comprehensive Survey

3 citations · 5 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study

Shahana Akter, Yatharth Vohra, Ankita Shukla +1

Multi-label topic classification without labeled training data is a challenging task, specially when documents contain complex relational information. We present a zero-shot multi-…

cs.CL2025

Instructional Goal-Aligned Question Generation for Student Evaluation in Virtual Lab Settings: How Closely Do LLMs Actually Align?

R. Alexander Knipper, Indrani Dey, Souvika Sarkar +3

Virtual Labs offer valuable opportunities for hands-on, inquiry-based science learning, yet teachers often struggle to adapt them to fit their instructional goals. Third-party mate…

cs.CL2025

Investigating Hallucination in Conversations for Low Resource Languages

Amit Das, Md. Najib Hasan, Souvika Sarkar +7

Large Language Models (LLMs) have demonstrated remarkable proficiency in generating text that closely resemble human writing. However, they often generate factually incorrect state…

cs.CL2025

Pitfalls of Evaluating Language Models with Open Benchmarks

Md. Najib Hasan, Md Mahadi Hassan Sibat, Mohammad Fakhruddin Babar +3

Open Large Language Model (LLM) benchmarks, such as HELM and BIG-Bench, provide standardized and transparent evaluation protocols that support comparative analysis, reproducibility…

cs.CL2025

Zero-Shot Multi-Label Classification of Bangla Documents: Large Decoders Vs. Classic Encoders

Souvika Sarkar, Md. Najib Hasan, Santu Karmaker

Bangla, a language spoken by over 300 million native speakers and ranked as the sixth most spoken language worldwide, presents unique challenges in natural language processing (NLP…

cs.CL20242 cited

Investigating Annotator Bias in Large Language Models for Hate Speech Detection

Amit Das, Zheng Zhang, Najib Hasan +12

Data annotation, the practice of assigning descriptive labels to raw data, is pivotal in optimizing the performance of machine learning models. However, it is a resource-intensive…