LLM knows when it hallucinates in advance, and this can be used to exclude hallucinations.

TLDR: prompt "predict the hallucination level of each item in the bibliography list and do not include items expected to have level 3 or above" works. 

I performed an experiment: I asked Claude 3.7 Sonnet to write the full bibliography of Bostrom. Around the 70th article, it started hallucinating. I then sent the results to GPT-4.5 and asked it to mark hallucinations and estimate the hallucination chances from 1 to 10 (where 10 is the maximal level of hallucination). It correctly identified hallucinations.

After that, I asked Sonnet 3.7 in another window to find the hallucination level in its own previous answer, and it gave almost the same answers as GPT-4.5. The difference was mostly about exact bibliographical data of some articles, and at first glance, it matched 90% of the data from GPT-4.5. I also checked the real data through Google Scholar manually.

After that, I asked Sonnet to write down the bibliography again but add a hallucination rating after each item. It again started hallucinating articles soon, but to my surprise, it gave correct answers ratings of 1-2 and incorrect ones ratings of 3-5 level of hallucination.

In the next step, I asked it to predict in advance which level of hallucination the next item would have and, if it was 3 or above, not to include it in the list. And it worked! It doesn't solve the problem of hallucinations completely but lowers their level about 10 times. Obviously, it can sometimes hallucinate the level of hallucinations too.

Maybe I can ask meta: predict the level of hallucinations in your hallucination estimate.