memory in language models

**Memory in language models** is the **capacity of language models to store and retrieve information from parameters, context, and internal state dynamics** - memory behavior underpins factual recall, in-context learning, and long-context reasoning. **What Is Memory in language models?** - **Types**: Includes parametric memory in weights and contextual memory in current prompt tokens. - **Retrieval**: Attention and MLP pathways jointly transform cues into recalled outputs. - **Timescales**: Memory operates across short local context and long-range sequence dependencies. - **Analysis**: Studied with probing, tracing, and editing interventions. **Why Memory in language models Matters** - **Capability**: Memory quality strongly affects factuality and task completion consistency. - **Safety**: Memory pathways influence memorization, privacy, and leakage risk. - **Interpretability**: Understanding memory structure is central to mechanistic transparency. - **Optimization**: Guides architectural and training changes for better long-context performance. - **Governance**: Memory behavior informs update and correction strategies. **How It Is Used in Practice** - **Benchmarking**: Evaluate both parametric recall and context-dependent retrieval tasks. - **Intervention**: Use editing and ablation to separate parameter memory from context memory effects. - **Monitoring**: Track memory-related error classes during model updates and deployment. Memory in language models is **a foundational concept for understanding language model behavior and limits** - memory in language models should be analyzed as a multi-source system spanning weights, context, and computation paths.