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Comment by lins1909 | Hacker News Reader

lins1909

Uhm, I'm very confused by the last part of your comment. You put it into an LLM to...understand the tone? Is that supposed to convince me of something?

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They phrased it poorly, but from context it seems clear they intend the LLM as a less biased third-party measure of the tone which agrees with their own assessment.

which they very much are not

Less biased? Maybe on political questions, etc, but this is a classic NLP task and I expect LLMs to be very reliable here

LLMs are predictive engines based on Reddit data, what they generate is very far from neutral

Reality check. The confirmation that the author is not alone in having this particular feeling by using an LLM as a proxy.

Using an LLM as a proxy for an 'unbiased' source of personal confirmation bias is perhaps the most common 'use-case' for an LLM.

I guess they use an LLM to work out the tone of prose they read. These are strange times.

Very weird indeed. People must not realize that you can completely change the response you get back from an LLM by how you ask questions. Any bias can implicitly be implanted in the question you ask and drastically modify the response. This is what I got Gemini to say about the article:

  The author’s tone in this piece can be described as brutally candid, deeply relieved, and unapologetically sarcastic.

very different from "The overall tone is deeply personal, cathartic, biting, and polemical, with flashes of humor and a deliberate attempt to soften the ending."

this is what worries me. I have friends that love what AI tells them about their personal pet 'thing' and how awesome it is. Yet not one of them has even tried once to get the AI to criticise it's own answers, and hence learn that you can trivially get an AI to make a convincing-_sounding case for any point of view.

I tell them to try, and they laugh at me as they roll their eyes and waffle on about 'tricking' the AI like its some kind of hacking.

Personally I don't think that matters, because the article is problematic enough when it can be read like ad hominem. Assuming that the question was phrased reasonably neutrally (but not necessarily free of hidden biases), the fact that LLM concluded so is an enough evidence here. Also as a non-LLM data point, I felt roughly same (especially the "softening" bit).

Not very different, mind you.

I don't really agree with using LLMs to do this but it correctly identified the attempt to soften the ending, which is to my mind significant in the whole piece; this person wants to repeat and frame unkind things he's heard, say unkind things, and then assert that he wasn't doing either.

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