Unfortunately this has just been the reality over the last couple years. People just ignore the hallucination problem (or try to say it isn't a big deal). And yet we have seen time and time again examples of these models being given something, told to summarize it, and still hallucinate important details. So you can't even make the argument that its data is flawed or something.

These models will interject information from their training whether or it is relevant or not. This is just due to the nature of how these models work.

Anyone trying to argue that it doesn't happen that often or anything is missing the key problem. Sure it may be right most of the time, but all that does is build a false sense of security and eventually you stop double checking or clicking through to a source. Whether it is a search result, manipulating data, or whatever.

This is made infinitely worse when these summaries are one and done, a single user is going to see the output and no one else will see it to fact check. It isn't like an article being wrong that everyone reading it is reading the same article, can then comment that something is wrong, it get updated, and so on and so forth. That feedback loop is non-existent with these models