I would like to propose a new method for managing conversational context in language models that could significantly enhance their efficiency and performance.
Concept Overview:
The idea involves implementing a context compression technique and improving context retrieval efficiency. This approach includes:
- Normal Processing: The model processes and outputs messages as usual, handling user input and generating responses.
- Context Compression: After each interaction, the model compresses the context to a more manageable size by summarizing key details. This involves:
- Using bullet points or concise lists to capture essential information.
- Employing a special flag to indicate when context compression has occurred.
- Preserving the original chat log for user reference and model access.
- Pinning IDs: Assigning a unique ID or number to each chat message that aligns with its compressed counterpart. This allows the model to access specific messages directly without needing to process the entire chat log.
Benefits:
- Increased Context Length Limit: Compressed context allows for handling longer interactions effectively by extending the effective length limit.
- Reduced Input Noise: Compression eliminates redundant or less relevant information, focusing on critical details and improving processing efficiency.
- Enhanced Performance: Streamlined contexts ensure subsequent interactions are based on clear and relevant data, reducing overhead and improving model responses.
- Efficient Context Retrieval: Pinning IDs to messages enables quick and precise retrieval of specific context without processing the full chat log, optimizing access to relevant information.
Implementation Considerations:
- Develop a method to summarize and condense context effectively while retaining essential details.
- Design a mechanism to indicate context updates and manage the original chat log.
- Implement a system for assigning and referencing unique IDs to facilitate efficient retrieval of specific messages.
I believe this approach could offer significant improvements in managing large conversations and optimizing model performance.
As a bonus when significantly reducing the overall context length and noise should be a large reduction in power usage.