Long Context Modeling with Ranked Memory-Augmented Retrieval
This addresses the challenge of handling extended contexts in language models, which is crucial for applications like document analysis or conversation systems, and represents an incremental improvement with novel scoring and re-ranking methods.
The paper tackles the problem of long-term memory management in language models by introducing a framework that dynamically ranks memory entries based on relevance, achieving state-of-the-art results on standard benchmarks.
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.