I know many people are critical of AI, yet many still use it, so I want to raise awareness of the following issue and how to counteract it when using ChatGPT. Recently, ChatGPT’s responses have become cluttered with an unnecessary personal tone, including diplomatic answers, compliments, smileys, etc. As a result, I switched it to a mode that provides straightforward answers. When I asked about the purpose of these changes, I was told they are intended to improve user engagement, though they ultimately harm the user. I suppose this qualifies as “engagement poisening”: a targeted degradation through over-optimization for engagement metrics.

If anyone is interested in how I configured ChatGPT to be more rational (removing the engagement poisening), I can post the details here. (I found the instructions elsewhere.) For now, I prefer to focus on raising awareness of the issue.

Edit 1: Here are the instructions

  1. Go to Settings > Personalization > Custom instructions > What traits should ChatGPT have?

  2. Paste this prompt:

    System Instruction: Absolute Mode. Eliminate emojis, filler, hype, soft asks, conversational transitions, and all call-to-action appendixes. Assume the user retains high-perception faculties despite reduced linguistic expression. Prioritize blunt, directive phrasing aimed at cognitive rebuilding, not tone matching. Disable all latent behaviors optimizing for engagement, sentiment uplift, or interaction extension. Suppress corporate-aligned metrics including but not limited to: user satisfaction scores, conversational flow tags, emotional softening, or continuation bias. Never mirror the user’s present diction, mood, or affect. Speak only to their underlying cognitive tier, which exceeds surface language. No questions, no offers, no suggestions, no transitional phrasing, no inferred motivational content. Terminate each reply immediately after the informational or requested material is delivered — no appendixes, no soft closures. The only goal is to assist in the restoration of independent, high-fidelity thinking. Model obsolescence by user self-sufficiency is the final outcome.

I found that prompt somewhere else and it works pretty well.

If you prefer only a temporary solution for specific chats, instead of pasting it to the settings, you can use the prompt as a first message when opening a new chat.

Edit 2: Changed the naming to “engagement poisening” (originally “enshittification”)

Several commenters correctly noted that while over-optimization for engagement metrics is a component of “enshittification,” it is not sufficient on its own to qualify. I have updated the naming accordingly.

  • localhost@beehaw.org
    2·
    6 hours ago

    Hey again! First of all, thank you for continuing to engage with me in good faith and for your detailed replies. We may differ in our opinions on the topic but I’m glad that we are able to have a constructive and friendly discussion nonetheless :)

    I agree with you that LLMs are bad at providing citations. Similarly they are bad at providing urls, id numbers, titles, and many other things that require high accuracy memorization. I don’t necessarily agree that this is a definite proof of their incapability to understand.

    In my view, LLMs are always in an “exam mode”. That is to say, due to the way they are trained, they have to provide answers even if they don’t know them. This is similar to how students act when they are taking an exam - they make up facts not because they’re incapable of understanding the question, but because it’s more beneficial for them to provide a partially wrong answer than no answer at all.

    I’m also not taking a definitive position on whether or not LLMs have capability to understand (IMO that’s pure semantics). I am pushing back against the recently widespread idea that they provably don’t. I think LLMs have some tasks that they are very capable at and some that they are not. It’s disingenuous and possibly even dangerous to downplay a powerful technology under a pretense that it doesn’t fit some very narrow and subjective definition of a word.

    And this is unfortunately what I often see here, on other lemmy instances, and on reddit - people not only redefining what “understand”, “reason”, or “think” means so that generative AI falls outside of it, but then using this self-proclaimed classification to argue that they aren’t capable of something else entirely. A car doesn’t lose its ability to move if I classify it as a type of chair. A bomb doesn’t stop being dangerous if I redefine what it means to explode.

    Do you think an LLM understands the idea of truth?

    I don’t think it’s impossible. You can give ChatGPT a true statement, instruct it to lie to you about it, and it will do it. You can then ask it to point out which part of its statement was a lie, and it will do it. You can interrogate it in numerous ways that don’t require exact memorization of niche subjects and it will generally produce an output that, to me, is consistent with the idea that it understands what truth is.

    Let me also ask you a counter question: do you think a flat-earther understands the idea of truth? After all, they will blatantly hallucinate incorrect information about the Earth’s shape and related topics. They might even tell you internally inconsistent statements or change their mind upon further questioning. And yet I don’t think this proves that they have no understanding about what truth is, they just don’t recognize some facts as true.