Sure, here you go:
https://www.youtube.com/watch?v=hhiLw5Q_UFg
Summarizing:
1. Obviously you can't completely "solve" truthfulness because people disagree on what is and is not true. But you can go a long way.
2. The models do know what they don't know. Their level of uncertainty is not only expressed in the final token logprobs but also seems to be reified somehow, such that they can express their own level of certainty in words.
3. Many of the problems are introduced by subtle issues introduced during training which effectively teach the AI to guess. One example is by training on QA data sets where the answer is never "I don't know". RLHF introduces its own problems because the human trainers don't know what the model knows, so may reward it for getting a correct answer by guessing.
4. Many failures are caused by lack of access to information. Giving the LLM tools to let it search the web and access other databases can help a lot, and it's relatively easy to do.
5. In some cases you want it to guess. Like, it's much more useful when coding for it to spit out a screenful of code that has one or two minor errors, than to just refuse to try at all because the result might not be perfect.