(For clarity I’ll re-emphasise that my top comment is the result of misreading the word “documents” out, so I’m speaking on general grounds about AI “summaries”, not just about AI “summaries” of documents.)
The key here is that the LLM is likely to hallucinate the claims of the text being shortened, but not the topic. So provided that you care about the later but not the former, in order to decide if you’re going to read the whole thing, it’s good enough.
And that is useful in a few situations. For example, if you have a metaphorical pile of a hundred or so scientific papers, and you only need the ones about a specific topic (like “Indo-European urheimat” or “Argiope spiders” or “banana bonds”).
That backtracks to the OP. The issue with using AI summaries for documents is that you typically know the topic at hand, and you want the content instead. That’s bad because then the hallucinations won’t be “harmless”.
But the claims of the text are often why you read it in the first place! If you have a hundred scientific papers you’re going to read the ones that make claims either supporting or contradicting your research.
You might as well just skim the titles and guess.
But the claims of the text are often why you read it in the first place!
By “not caring about the former” [claims], I mean in the LLM output, because you know that the LLM will fuck them up. But it’ll still somewhat accurately represent the topic of the text, and you can use this to your advantage.
You might as well just skim the titles and guess.
Nirvana fallacy.
Unless it doesn’t accurately represent the topic, which happens, and then a researcher chooses not to read the text based on the chatbot’s summary.
Nirvana fallacy.
All these chatbots do is guess. I’m just saying a researcher might as well cut out the hallucinating middleman.
not reading the fucking sidebar and thinking this is high school debate club fallacy
