Papers by Basem Suleiman
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 . |
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. |