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Author

Sheza Munir

Observed name: Munir, Sheza
ORCID: 0009-0008-2175-254X
Observed ORCID: 0009-0008-2175-254X
OpenAlex ID: A5114633693
Deepfake Defense: Constructing and Evaluating a Specialized Urdu Deepfake Audio Dataset
2024 · Sheza Munir, Wassay Sajjad, et al. · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · 7
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VeriFact: Enhancing Long-Form Factuality Evaluation with Refined Fact Extraction and Reference Facts
2025 · Xin Liu, Lechen Zhang, et al. · Conference on Empirical Methods in Natural Language Processing (EMNLP) · 4
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FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation
2025 · Farima Fatahi Bayat, Lechen Zhang, et al. · Proceedings of the conference - Association for Computational Linguistics. Meeting · 2
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Activating the Annotator: Operationalising Lived Experience in Data Annotation
2026 · Munir, Sheza, Mothilal, Ramaravind Kommiya, et al. · arXiv (Cornell University)
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The Consensus Trap: Dissecting Subjectivity and the “Ground Truth” Illusion in Data Annotation
2026 · Sheza Munir, Benjamin Mah, et al. · ACM Conference on Fairness, Accountability, and Transparency (FAccT)
Are You the A-hole? A Fair, Multi-Perspective Ethical Reasoning Framework
2026 · Munir, Sheza, Rodoshi, Ahanaf, et al. · arXiv (Cornell University)
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Dharma, Data and Deception: An LLM-Powered Rhetorical Analysis of Cow-Urine Health Claims on YouTube
2026 · Munir, Sheza, Kandala, Ratna, et al. · arXiv (Cornell University)
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When Cow Urine Cures Constipation on YouTube: Limits of LLMs in Detecting Culture-specific Health Misinformation
2026 · Khan, Anamta, Kandala, Ratna, et al. · arXiv (Cornell University)
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The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation
2026 · Munir, Sheza, Mah, Benjamin, et al. · arXiv (Cornell University)
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Works count: 12
Citation count: 13
H-index: 2
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