By representing prior conversations as symbols within a knowledge graph ($conversations), and using an internal method to traverse this graph, an LLM can provide contextually rich answers with lower overhead. This approach aims to mimic long-term memory more effectively than current methods.
- Contextual Understanding: By integrating knowledge graphs, an AI or LLM can leverage the structured relationships and properties of entities within the graph to better understand the context of user queries or content, leading to more accurate and relevant responses.
- Data Efficiency: Knowledge graphs can streamline the data processing by directly using relationships between nodes (entities) rather than parsing large volumes of text to extract meaning. This can reduce computational overhead and speed up response times.
- Continual Learning: A knowledge graph can be dynamically updated with new information, allowing the AI system to continually l
