Papers by Ali Anwar
POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization (2026.findings-acl)
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Usman Naseem, Robert Geislinger, Juan Ren, Sarah Kohail, Rudy Alexandro Garrido Veliz, P Sam Sahil, Yiran Zhang, Idris Abdulmumin, Marco Antonio Stranisci, Özge Alacam, Cengiz Acarturk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Simona Frenda, Alessandra Teresa Cignarella, Elena Tutubalina, Oleg Rogov, Aung Kyaw Htet, Xintong Wang, Surendrabikram Thapa, Kritesh Rauniyar, Tanmoy Chakraborty, MD Arfeen Zeeshan, Dheeraj Kodati, Satya Keerthi, Sahar Moradizeyveh, Firoj Alam, Md Arid Hasan, Syed Ishtiaque Ahmed, Ye Kyaw Thu, Shantipriya Parida, Ihsan Ayyub Qazi, Lilian Diana Awuor Wanzare, Nelson Odhiambo Onyango, Clemencia Siro, Jane Wanjiru Kimani, Ibrahim Said Ahmad, Adem Chanie Ali, Martin Semmann, Chris Biemann, Shamsuddeen Hassan Muhammad, Seid Muhie Yimam
| Challenge: | polarization is a pervasive threat to democratic institutions, civil discourse, and social cohesion worldwide . most existing datasets focus on English or high-resource languages, reflecting a widespread trend across NLP tasks . |
| Approach: | They propose a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. |
| Outcome: | The proposed dataset analyzes polarization detection, type, and manifestation using a variety of annotation platforms adapted to each cultural context. |
Accelerating LLM Reasoning via Early Rejection with Partial Reward Modeling (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly relied upon for solving complex reasoning tasks. |
| Approach: | They propose to use Process Reward Models to scale inference time compute by generating in parallel . they propose to provide early signals that enable the rejection of suboptimal candidates before full generation of step is complete. |
| Outcome: | The proposed method achieves 1.4 – 9 reduction in inference FLOPs without degrading final performance. |
Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly integrated into everyday workflows . a recent study found that LLMs exhibit distinct personality-like traits that affect user engagement . |
| Approach: | They evaluated 32 LLM users for four collaborative tasks and found significant preferences . they found that rationalists preferred GPT-4, while idealists favored Claude 3.5 . |
| Outcome: | The results show that users with different personality traits prefer certain LLMs over others. |
Sem-DPO: Mitigating Semantic Inconsistency in Preference Optimization for Prompt Engineering (2026.findings-acl)
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Anas Mohamed, Azal Ahmad Khan, Xinran Wang, Ahmad Faraz Khan, Shuwen Ge, Saman Bahzad Khan, Ayaan Ahmad, Ali Anwar
| Challenge: | Direct Preference Optimization (DPO) is an off-policy alternative to RL for automatic prompt engineering, but its token-level regularization leaves semantic inconsistency unchecked as prompts that win higher preference scores can still drift away from the user’s intended meaning. |
| Approach: | They propose a variant of Direct Preference Optimization that preserves semantic consistency while maintaining its simplicity and efficiency. |
| Outcome: | The proposed model outperforms state-of-the-art prompt optimization baselines and several DPO variants on three standard text-to-image prompt-optimization benchmarks and three language models. |
AID: Adaptive Integration of Detectors for Safe AI with Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) are increasingly used to generate human-like text, but safety concerns have emerged with the deployment of LLMs. |
| Approach: | They propose an approach that orchestrates the strengths of multiple pretrained detectors to ensure comprehensive effectiveness in diverse scenarios. |
| Outcome: | The proposed approach can improve the area under the curve (AUC) by 0.07 to 0.21, with a median of 0.12, compared to the best individual detectors developed for specific safety aspects. |