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Long Context Modeling with Ranked Memory-Augmented Retrieval
Created by
Haebom
Author
Ghadir Alselwi, Hao Xue, Shoaib Jameel, Basem Suleiman, Hakim Hacid, Flora D. Salim, Imran Razzak
Outline
This paper emphasizes that long-term memory management is crucial for language models that handle extended contexts, and presents a novel framework for dynamically ranking memory items based on relevance. Unlike previous works, we introduce a novel relevance score and point-wise re-ranking model for key-value embeddings, inspired by learning-ranking techniques in the field of information retrieval. Enhanced Ranked Memory Augmented Retrieval (ERMAR) achieves state-of-the-art results on standard benchmarks.
Takeaways, Limitations
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Takeaways:
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We present a novel method that effectively applies learning-ranking techniques from the field of information retrieval to long-term memory management of language models.
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Achieving improved performance over existing approaches through relevance-based dynamic memory ranking.
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It delivers cutting-edge performance in standard benchmarks and provides an effective solution to the problem of long-term memory management.
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Limitations:
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Additional experiments and analysis are needed to determine the generalization performance of the proposed model.
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Further performance evaluation on different types of datasets is needed.
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Analysis of the complexity and computational cost of the point-by-point re-ranking model is needed.
