Effective long-term memory management is crucial for language models handling extended contexts. We introduce the Enhanced Ranked Memory Augmented Retrieval (ERMAR) framework, which dynamically ranks memory entries based on relevance. Unlike prior models, ERMAR employs a novel relevance scoring mechanism and a pointwise re-ranking model for key-value embeddings, inspired by learning-to-rank techniques in information retrieval. By integrating historical usage patterns and adaptive retrieval, ERMAR achieves state-of-the-art results on standard benchmarks, demonstrating superior scalability and performance in long-context tasks.

翻译：有效的长期记忆管理对于处理扩展上下文的语言模型至关重要。我们提出了增强型排序记忆增强检索（ERMAR）框架，该框架根据相关性动态对记忆条目进行排序。与先前模型不同，ERMAR采用了一种新颖的相关性评分机制以及针对键值嵌入的逐点重排序模型，这一设计受信息检索中学习排序技术的启发。通过整合历史使用模式与自适应检索，ERMAR在标准基准测试中取得了最先进的结果，展示了其在长上下文任务中的卓越可扩展性与性能表现。