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
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    17 hours ago

    In my sense of “understanding” it’s actually knowing the content and context of something, being able to actually subject it to analysis and explain it accurately and completely.

    This is something that sufficiently large LLMs like ChatGPT can do pretty much as well as non-expert people on a given topic. Sometimes better.

    This definition is also very knowledge dependent. You can find a lot of people that would not meet this criteria, especially if the subject they’d have to explain is arbitrary and not up to them.

    Can you prove otherwise?

    You can ask it to write a poem or a song on some random esoteric topic. You can ask it to play DnD with you. You can instruct it to write something more concisely, or more verbosely. You can tell it to write in specific tone. You can ask follow-up questions and receive answers. This is not something that I would expect of a system fundamentally incapable of any understanding whatsoever.

    But let me reverse this question. Can you prove that humans are capable of understanding? What test can you posit that every English-speaking human would pass and every LLM would fail, that would prove that LLMs are not capable of understanding while humans are?

    • Zaleramancer@beehaw.org
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      5 hours ago

      And, yes, I can prove that a human can understand things when I ask: Hey, go find some books on a subject, then read them and summarize them. If I ask for that, and they understood it, they can then tell me the names of those books because their summary is based on actually taking in the information, analyzing it and reorganizing it by apprehending it as actual information.

      They do not immediately tell me about the hypothetical summaries of fake books and then state with full confidence that those books are real. The LLM does not understand what I am asking for, but it knows what the shape is. It knows what an academic essay looks like and it can emulate that shape, and if you’re just using an LLM for entertainment that’s really all you need. The shape of a conversation for a D&D npc is the same as the actual content of it, but the shape of an essay is not the same as the content of that essay. They’re too diverse, and they have critical information in them and they are about that information. The LLM does not understand the information, which is why it makes up citations- it knows that a citation fits in the pattern, and that citations are structured with a book name and author and all the other relevant details. None of those are assured to be real, because it doesn’t understand what a citation is for or why it’s there, only that they should exist. It is not analyzing the books and reporting on them.

    • Zaleramancer@beehaw.org
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      5 hours ago

      Hello again! So, I am interested in engaging with this question, but I have to say: My initial post is about how an LLM cannot provide actual, real citations with any degree of academic rigor for a random esoteric topic. This is because it cannot understand what a citation is, only what it is shaped like.

      An LLM deals with context over content. They create structures that are legible to humans, and they are quite good at that. An LLM can totally create an entire conversation with a fictional character in their style and voice- that doesn’t mean it knows what that character is. Consider how AI art can have problems that arise from the fact that they understand the shape of something, but they don’t know what it actually is- that’s why early AI art had a lot of problems with objects ambigiously becoming other objects. The fidelity of these creations has improved with the technology, but that doesn’t imply understanding of the content.

      Do you think an LLM understands the idea of truth? Do you think if you ask it to say a truthful thing, and be very sure of itself and think it over, it will produce something that’s actually more accurate or truthful- or just something that has the language hall-marks of being truthful? I know that an LLM will produce complete fabrications that distort the truth if you expect a base-line level of rigor from them, and I proved that above, in that the LLM couldn’t even accurately report the name of a book it was supposedly using as a source.

      What is understanding, if the LLM can make up an entire author, book and bibliography if you ask it to tell you about the real world?