Long Context Modeling With Ranked Memory-augmented Retrieval
Ghadir Alselwi, Hao Xue, Shoaib Jameel, Basem Suleiman, Hakim Hacid, Flora D. Salim, Imran Razzak . Arxiv 2025 – 0 citations
[Paper]
Effective long-term memory management is crucial for language models handling extended contexts. We introduce a novel framework that dynamically ranks memory entries based on relevance. Unlike previous works, our model introduces a novel relevance scoring and a pointwise re-ranking model for key-value embeddings, inspired by learning-to-rank techniques in information retrieval. Enhanced Ranked Memory Augmented Retrieval ERMAR achieves state-of-the-art results on standard benchmarks.