Papers by Muhammad Rashid
PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering (2024.findings-acl)
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| Challenge: | Existing work on Temporal Question Answering (TQA) has focused on questions anchored to specific timestamps or events. |
| Approach: | They introduce a benchmark to address present-anchored temporal QA (PATQA) which includes single and multi-hop temporal questions. |
| Outcome: | The proposed model can be automatically refreshed by re-running SPARQL queries on a knowledge graph. |
EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated dominant performance in text re-ranking. |
| Approach: | They propose a suite of budget-constrained methods to perform text re-ranking using LLMs. |
| Outcome: | The proposed method outperforms other budget-aware methods on four datasets. |
A Culturally-diverse Multilingual Multimodal Video Benchmark & Model (2025.emnlp-main)
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Bhuiyan Sanjid Shafique, Ashmal Vayani, Muhammad Maaz, Hanoona Abdul Rasheed, Dinura Dissanayake, Mohammed Irfan Kurpath, Yahya Hmaiti, Go Inoue, Jean Lahoud, Md. Safirur Rashid, Shadid Intisar Quasem, Maheen Fatima, Franco Vidal, Mykola Maslych, Ketan Pravin More, Sanoojan Baliah, Hasindri Watawana, Yuhao Li, Fabian Farestam, Leon Schaller, Roman Tymtsiv, Simon Weber, Hisham Cholakkal, Ivan Laptev, Shin’ichi Satoh, Michael Felsberg, Mubarak Shah, Salman Khan, Fahad Shahbaz Khan
| Challenge: | Large multimodal models have gained attention for their effectiveness to understand and generate descriptions of visual content. |
| Approach: | They propose a multilingual Video LMM benchmark to evaluate video LMMs across 14 languages . they also introduce a machine translated multilingual video training set . |
| Outcome: | The proposed video LMM benchmark is designed to evaluate video Lmms across 14 languages including Arabic, Bengali, Chinese, English, French, German, Hindi, Japanese, Russian, Sinhala, Spanish, Swedish, Tamil, and Urdu. |