Research Applicability and Productivization Challenges
While KBLaM has performed well in academic experiments, it is officially and explicitly recommended for research use at its current stage. The main limitations include: the quality of knowledge embedding relies on the degree of structuring of the source data, and non-normalized knowledge entries may lead to response bias (tests show that noisy data decreases accuracy by 40%); high-end GPUs with more than 80GB of video memory are required to process very large knowledge bases; and the system lacks a sophisticated confidence calibration mechanism, which may produce overconfident questions beyond the scope of the knowledge base answers to questions beyond the scope of the knowledge base. These characteristics make it more suitable for controlled research environments, such as the Microsoft team that has used it to explore the efficacy of combining medical knowledge graphs with LLM.
This answer comes from the articleKBLaM: An Open Source Enhanced Tool for Embedding External Knowledge in Large ModelsThe
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