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That would be a huge result it if works. The paper: "Our approach is to train a classifier that outputs the probability that a statement is truthful, based on
by Animats 2y ago
That would be a huge result it if works. The paper:
"Our approach is to train a classifier that outputs the probability that a statement is truthful, based on the hidden layer activations of the LLM as it reads or generates the statement. Experiments demonstrate that given a set of test sentences, of which half are true and half false, our trained classifier achieves an average of 71% to 83% accuracy labeling which sentences are true versus false, depending on the LLM base model."[1]
It's not entirely clear what they measured and whether what they measured is useful. Is this a major result?
[1] https://arxiv.org/pdf/2304.13734 https://arxiv.org/pdf/2304.13734
- jncfhnb 2y agoLooks like they tested against statements that were obviously false and written by humans; not hallucinated statements created by the model that happened to be false. So, no, it doesn’t seem very interesting at all to me.
- Animats 2y agoThat's what I thought I read, too. Not too useful. We really need LLMs with a confidence metric. This doesn't seem to be it.