I would not suggest anyone to use ChatGPT outputs for actual knowledge at this point.
I would not suggest anyone to use ChatGPT outputs for actual knowledge at this point.
> "Discovering Latent Knowledge in Language Models Without Supervision" Existing techniques for training language models can be misaligned with the truth: if we train models with imitation learning, they may reproduce errors that humans make; if we train them to generate text that humans rate highly, they may output errors that human evaluators can't detect. We propose circumventing this issue by directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way.
https://arxiv.org/abs/2212.03827
In other words the model already tries to predict the truth because it is useful in next token prediction, but we need to find a way to detect the 'truth alignment' in its activations.
aws cloudfront update-distribution --id <distribution-id> --distribution-config <new-config> --no-reset-origin-access-identity
Took me a while to figure out that the parameter --no-reset-origin-access-identity was not only not working. But it did never exist on any version of the cli tool.
But for this problem (CF-Distribution lost OAC settings when updating the root file), all the google fu in the world did not help me. It turned out that I had to update my aws-cli and my problem went away. Apparently no one else on the internet had that problem, so only my gut could help me figure it out.
Meaning, if all the facts exist in the prompt then the likelihood of synthesizing fiction is diminished.
There are a number of ways to use the principle of analytic augmentation to add most or if not all of the facts required for a truthful response, ranging from simple “prompt engineering” to evaluating code to document embedding in latent space.
For example, if you use prompt engineering to k-shot a task to turn math word problems into executable JavaScript, meaning LLMs are only translators and the computations are done by a software interpreter, then the results are much more likely to be truthful.
Sampling from a number of variations on a prompt can lead to a more accurate outcome if say 1:10 times the translation attempt has a different answer.