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Such a simple yet powerful idea in new Meta paper.
Says that likelihood is the wrong test of truth for LLMs, and that answers which stay the same across contexts deserve more trust.
that LLMs are trained to predict the most likely next token, so they learn what is common in text, not what is true. A falsehood repeated often in training data becomes probable, while rare true events get discounted.
Drawing on physics and philosophy, the paper treats a fact as real if it survives a change of viewpoint. The model answers from several viewpoints, such as 3 medical specialists, keeps what every answer shares, and abstains if nothing is shared.
The paper runs no experiments, so its proof is worked examples. On a single binary example, a loss of 0.67 means 100% accuracy, while a nearly equal loss of 0.71 means 0%.
Before trusting a confident answer, check whether it survives changes in persona, framing, or language.