Introducing Lamini Memory Tuning, a new approach to embedding facts into LLMs that improves factual accuracy and reduces hallucinations. Lamini Memory Tuning achieves 95% accuracy compared to 50% with other approaches, and reduces hallucinations from 50% to 5%. It overcomes the challenge of achieving precise factual accuracy while maintaining generalization capabilities. The method involves tuning millions of expert adapters with precise facts on top of open-source LLMs, resulting in high accuracy, high speed, and low cost.
Table of contents
Prompting and RAG: necessary but not sufficientInstruction fine-tuning: the wrong tool for the jobLamini Memory Tuning: near-perfect fact recall via 1 million-way MoEResultsA new frontierStart using Lamini Memory Tuning1 Comment
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