I am not an expert in modern NLP SOTA but from what I see these models do not work that well.
You didn't summarize the article but took some of the most important points and shared what really mattered. It may not be perfect but required a real life experience. I am afraid there will always be a chance of getting "techno-babble" from our models, at least until they we get to some sort of AGI.
Look at your first line:
> Most of the article appears to be about what does not work.
No model will decide to include that info and it is vital for me to understand why you chose just a few lines out of it. We are still long way out from trusting in reliable LLMs.
I asked chatgpt to pick the most important 2 or 3 lines of text from the article. It returned:
1. "Fortunately, there is advice out there on how to break the ice with strangers. Unfortunately, it’s abysmal."
2. "Making contacts on a site like LinkedIn is a lot less stressful."
3. "The real secret is to save your energy for the people who are most likely to be interesting to you."
It got 2 out of 3 wrong basically. I'm not sure how to objectively measure/improve that in an automated way. I suppose some prompt might do better though?
I guess a second pass of the results through another LLM could potentially help like you suggest?
You didn't summarize the article but took some of the most important points and shared what really mattered. It may not be perfect but required a real life experience. I am afraid there will always be a chance of getting "techno-babble" from our models, at least until they we get to some sort of AGI.
Look at your first line:
> Most of the article appears to be about what does not work.
No model will decide to include that info and it is vital for me to understand why you chose just a few lines out of it. We are still long way out from trusting in reliable LLMs.