Papers by Abdul Hameed Azeemi
Language Model-Driven Data Pruning Enables Efficient Active Learning (2026.findings-eacl)
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| Challenge: | Existing data pruning methods for active learning are expensive and time-consuming. |
| Approach: | They propose a plug-and-play data pruning strategy that leverages language models to prune the unlabeled pool. |
| Outcome: | The proposed pruning strategy outperforms existing pruning methods on translation, sentiment analysis, topic classification, and summarization tasks on diverse datasets. |
Generalists vs. Specialists: Evaluating Large Language Models for Urdu (2024.findings-emnlp)
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| Challenge: | Urdu is underrepresented in natural language processing, yet it is underserved. |
| Approach: | They compare general-purpose models with special-purpose ones that have been fine-tuned on specific tasks. |
| Outcome: | The proposed models outperform general-purpose models on seven classification and seven generation tasks. |
Deepfake Defense: Constructing and Evaluating a Specialized Urdu Deepfake Audio Dataset (2024.findings-acl)
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Sheza Munir, Wassay Sajjad, Mukeet Raza, Emaan Abbas, Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
| Challenge: | Automatic speaker verification systems are facing escalating challenges due to deepfake attacks. |
| Approach: | They propose a Urdu deepfake audio dataset for deepfak detection focusing on two spoofing attacks – Tacotron and VITS TTS. |
| Outcome: | The proposed dataset evaluates two spoofing attacks in Urdu with a human evaluation to gauge whether people are able to distinguish deepfake audios from real (bonafide) audios. |
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation (2025.coling-main)
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| Challenge: | Active learning (AL) techniques reduce labeling costs for training neural machine translation models by selecting smaller representative subsets from unlabeled data for annotation. |
| Approach: | They propose an AL strategy that combines uncertainty and diversity for sentence selection. |
| Outcome: | The proposed method prioritizes diverse instances having high model uncertainty for annotation in early iterations. |