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Lamini Memory Tuning: 10x Fewer Hallucinations
Introducing Lamini Memory Tuning: 95% LLM Accuracy, 10x Fewer Hallucinations TLDR: - Lamini Memory Tuning is a new way to embed facts into LLMs that improves factual accuracy and reduces hallucinations to previously unachievable levels — for one Fortune 500 customer, Lamini Memory Tuning led to 95% accuracy compared to 50% with other approaches. Hallucinations were reduced from 50% to 5%.
Lamini Memory Tuning is a research breakthrough that overcomes a seeming paradox in the AI world: achieving precise factual accuracy (i.e. no hallucinations) while upholding the generalization capabilities that make LLMs valuable in the first place. Lamini Memory Tuning is a fundamentally different fine-tuning approach that effectively teaches any open-source LLM to be near-perfect on facts, while still maintaining its ability to be pretty good at everything else. The result is a sparsely activated model, called a Mixture of Memory Experts (MoME), that can scale to an enormous number of parameters at a fixed computational inference cost.
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