Comparison of Technical Advantages
- Architecture Simplification: the necessary retrieval module of the RAG is omitted and the knowledge is encoded directly in the model attention layer
- Delay reduction: end-to-end processing eliminates time-consuming retrieval, especially for real-time Q&A scenarios
- Greater integration of knowledge: Knowledge vectors involved in attention computation instead of RAG's patchwork processing
Typical Application Scenarios
Research: Embedded chemistry/medical specialty libraries enhance academic quiz accuracy;enterprise application: Convert internal documents into intelligent assistant knowledge sources;Educational Scenarios: Course materials directly enhance the answerability of teaching AI. Experiments have shown a positive effect on theFact-based questionsThe response accuracy was improved by 37%.
caveat
The current version is better at structured knowledge (e.g., glossaries, encyclopedia entries), and the processing effect on unstructured long documents has to be optimized, and it is recommended to cooperate with entity recognition preprocessing.
This answer comes from the articleKBLaM: An Open Source Enhanced Tool for Embedding External Knowledge in Large ModelsThe
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