A novel approach for training LLM models that suppress hallucinations and possess memory capabilities (without using RAG)

anon48749422
Tue, 22 Apr 2025 01:11:00 +0000

I’m envisioning a system composed of an “LLM teacher” and a “learning model.” The learning model is organized much like the human brain, with distinct specialized regions. The LLM teacher understands the role of each region and dynamically adjusts their learning intensities during training as needed.

In this architecture, a dedicated memory module—akin to the human hippocampus—is separated out, and by tuning learning strength, the LLM learns to distinguish between memory-based reasoning (non‑hallucinatory answers) and imaginative reasoning (hallucinatory answers).

Right now it’s just an idea and lacks detailed technical specifications, so it may not be practically feasible—but I hope you’ll take it as a fun thought experiment, and I’d love to hear what others think!

I’m not very good at English, so I’m not sure if my idea was conveyed clearly.

In summary, my idea is that the LLM determines which parts of the brain should be activated or deactivated depending on the situation and adjusts the intensity of training accordingly.
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