Memory in large language models (LLMs) involves managing context windows to enhance reasoning capabilities without the high costs associated with traditional training methods. The goal of Memory is to address challenges like “forgetting,” where LLMs struggle to retain context across interactions.
Key Concepts:
Forgetting Context:
Understanding how and why LLMs lose context, especially in multi-turn dialogues, and its impact on response accuracy. Forgetting occurs due to the limitations of fixed context windows, manifesting differently in single-turn (immediate forgetting) versus multi-turn interactions (progressive loss of context).
Prioritization of Context: Techniques for determining which parts of the context are most relevant and need to be retained, optimizing memory usage.
Time Length of Memory: Balancing how long memory should be maintained to ensure it remains useful and relevant over time.
Dynamic Memory Management: Adapting memory structures in real-time to accommodate evolving knowledge and interactions.
In-Context Memory: Memory tied to specific interactions, making it more relevant and easier to apply in particular scenarios.
Multi-turn Interactions: Addressing context retention across multiple interactions, emphasizing the importance of maintaining coherence over extended conversations.
Types of Memory:
Semantic Memory: Focuses on the meaning and Semantic Relationships between concepts, which is crucial for improving LLM reasoning and context understanding.
Hierarchical Memory: Balances immediate retrieval with long-term storage of information, enabling better performance in various applications.
Supports evolving and persistent memory systems tailored to specific tasks.