Researchers tackle AI fact-checking failures with new LLM training technique
Briefly

"They could give the model a genetics dataset and ask the model to generate a report on the gene variants and mutations it contains... the model begins generating new instructions and responses, calling on the latent expertise in its training data and using RAG to pull facts from external databases when necessary to ensure accuracy."
"The underlying problem is that LLMs are widely misunderstood. They are good at specific tasks but are not, nor were ever intended to be, uncomplicated fact- or truth-checking engines."
Read at Computerworld
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