Papers by Ghadir Alselwi

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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 .