Papers by Imran Razzak
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation (2025.acl-long)
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Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, Yutong Xie, Imran Razzak, Zongyuan Ge, Jionglong Su, Junjun He, Yu Qiao
| Challenge: | Existing multimodal large language models lack the ability to memorize, recall, and reason in sustained interactions. |
| Approach: | They propose a multimodal real-world conversation benchmark for evaluating open-ended abilities of multimodal large language models. |
| Outcome: | The proposed benchmarks show that the models perform better in open-ended conversations. |
Long Context Modeling with Ranked Memory-Augmented Retrieval (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) face a fundamental limitation in processing long-context scenarios due to quadratic complexity of attention mechanisms and increasing memory demands during generation. |
| Approach: | They propose a framework that dynamically ranks memory entries based on relevance . ERMAR employs a relevance scoring mechanism and a pointwise re-ranking model for key-value embeddings . |
| Outcome: | The proposed framework achieves state-of-the-art performance on benchmarks . it uses historical usage patterns and adaptive retrieval to improve performance . |
From Generation to Detection: A Multimodal Multi-Task Dataset for Benchmarking Health Misinformation (2025.findings-emnlp)
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| Challenge: | Infodemics and health misinformation have significant negative impact on individuals and society . generative AI has significantly accelerated the spread and expanded the reach of health misinfo . |
| Approach: | MM-Health is a large scale multimodal misinformation dataset in the health domain . it includes human-generated multimodal information and AI-generated multiplemodal information . |
| Outcome: | MM-Health is a large scale misinformation dataset in the health domain . it includes human-generated multimodal information and AI-generated content . |
Uncertainty Modelling in Under-Represented Languages with Bayesian Deep Gaussian Processes (2025.coling-main)
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| Challenge: | Existing methods for NLP modeling underrepresented languages are limited due to lack of training data and language complexities. |
| Approach: | They propose a new method that integrates prior knowledge and leverages kernel functions to quantify uncertainty in under-represented languages. |
| Outcome: | The proposed method improves prediction accuracy and measurement of uncertainty in under-represented languages. |
TAGS: A Test-Time Generalist–Specialist Framework with Retrieval-Augmented Reasoning and Verification (2026.findings-acl)
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Jianghao Wu, Feilong Tang, Yulong Li, Ming Hu, Haochen Xue, Shoaib Jameel, Zongyuan Ge, Yutong Xie, Imran Razzak
| Challenge: | Existing efforts to improve medical question answering performance follow two directions. |
| Approach: | They propose a framework that combines a generalist with a domain-specific specialist without any model fine-tuning or parameter updates. |
| Outcome: | The proposed framework boosts GPT-4o accuracy by 13.8%, deepseek-R1 by 16.8%, and improves a vanilla 7B model from 14.1% to 23.9%. |
Leveraging Taxonomy and LLMs for Improved Multimodal Hierarchical Classification (2025.coling-main)
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Shijing Chen, Mohamed Reda Bouadjenek, Usman Naseem, Basem Suleiman, Shoaib Jameel, Flora Salim, Hakim Hacid, Imran Razzak
| Challenge: | Multi-level Hierarchical Classification (MLHC) is a critical tool in modern data analysis. |
| Approach: | They propose a taxonomy-embedded transitional LLM-agnostic framework for multimodality classification that leverages large language models to enforce consistency across hierarchical levels. |
| Outcome: | The proposed framework improves on the MEP-3M dataset with various hierarchical levels compared to conventional models. |
A Knowledge-driven Adaptive Collaboration of LLMs for Enhancing Medical Decision-making (2025.emnlp-main)
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| Challenge: | Medical decision-making often involves integrating knowledge from multiple clinical specialties. static, pre-assigned roles hinder adaptability and dynamic knowledge integration. |
| Approach: | They propose a Knowledge-driven Adaptive Multi-Agent Collaboration framework that emulates large language models to emulate expert teamwork. |
| Outcome: | The proposed framework outperforms single-agent and advanced multi-agend methods on two real-world medical scenarios. |