It also mainly happens when the context is clear that we are collaborating on work that will require multiple iterations of review and feedback, like drafting chapters of a handbook.
I have seen ChatGPT ask questions immediately upfront when it relates to medical issues.
The users are being engineered more than the models are, and this isn't the only example.
In the case of medical questions it needs to know further details to provide a relevant diagnosis. That is how it was trained.
In other cases you can observe its reasoning process to see why it would decide to request further details.
I have never seen an LLM just ask questions for the sake of asking. It is always relevant in the context. I don't use them casually. Just wrote a couple of handbooks (~100 pages in a few days). Generating tens of thousands of tokens per session with Gemini.
- "Should I now give you the complete [result], fulfilling [all your demands]?"
- "Just say [go] and I will do it"
- "Do you want either [A, B, or C]"
- "In [5-15] minutes I will give you the complete result"
...
That's an example of what I'm talking about. Watch the reasoning process produce multiple options. That's what it is trained to do. That is problem solving, not "engagement". It requires more compute, not less. You see that more with the expensive models.
> "In [5-15] minutes I will give you the complete result"
I haven't seen that before and I don't see how it's relevant.
Fair point. Thanks for standing your ground and arguing so matter-of-factly with me! Appreciate it.
The optional choices happen when it tries to reason out a solution, but then finds it is making too many assumptions of unknown details about the user's system, preferences, goals, and so on. It's just a thought pattern that it has learned to emulate.
People here will argue that LLM's cannot truly "think", but they are good enough at emulating thinking.