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 “enshittification”.
If anyone is interested in how I configured ChatGPT to be more rational (removing the enshittification), I can post the details here. (I found the instructions elsewhere.) For now, I prefer to focus on raising awareness of the issue.
Edit: Here are the instructions
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Go to Settings > Personalization > Custom instructions > What traits should ChatGPT have?
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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.
As I understand it, most LLM are almost literally the Chinese rooms thought experiment. They have a massive collection of data, strong algorithms for matching letters to letters in a productive order, and sufficiently advanced processing power to make use of that. An LLM is very good at presenting conversation; completing sentences, paragraphs or thoughts; or, answering questions of very simple fact- they’re not good at analysis, because that’s not what they were optimized for.
This can be seen when people discovered that if ask them to do things like tell you how many times a letter shows up in a word, or do simple math that’s presented in a weird way, or to write a document with citations- they will hallucinate information because they are just doing what they were made to do: complete sentences, expand words along a probability curve that produces legible, intelligible text.
I opened up chat-gpt and asked it to provide me with a short description of how Medieval European banking worked, with citations and it provided me with what I asked for. However, the citations it made were fake:
The minute I asked it, I assume a bit of sleight of hand happened, where it’s been set up so that if someone asks a question like that it’s forwarded to a search engine that verifies if the book exists, probably using Worldcat or something. Then I assume another search is made to provide the prompt for the LLM to present the fact that the author does exist, and possibly accurately name some of their books.
I say sleight of hand because this presents the idea that the model is capable of understanding it made a mistake, but I don’t think it does- if it knew that the book wasn’t real, why would it have mentioned it in the first place?
I tested each of the citations it made. In one case, I asked it to tell me more about one of them and it ended up supplying an ISBN without me asking, which I dutifully checked. It was for a book that exists, but it didn’t share a title or author, because those were made up. The book itself was about the correct subject, but the LLM can’t even tell me what the name of the book is correctly; and, I’m expected to believe what it says about the book itself?
Chinese room is not what you think it is.
Searle’s argument is that a computer program cannot ever understand anything, even if it’s a 1:1 simulation of an actual human brain with all capabilities of one. He argues that understanding and consciousness are not emergent properties of a sufficiently intelligent system, but are instead inherent properties of biological brains.
“Brain is magic” basically.
Let me try again: In the literal sense of it matching patterns to patterns without actually understanding them.
If I were to have a discussion with a person responding to me like ChatGPT does, I would not dare suggest that they don’t understand the conversation, much less that they are incapable of understanding anything whatsoever.
What is making you believe that LLMs don’t understand the patterns? What’s your idea of “understanding” here?
What’s yours? I’m stating that LLMs are not capable of understanding the actual content of any words they arrange into patterns. This is why they create false information, especially in places like my examples with citations- they are purely the result of it creating “academic citation” sounding sets of words. It doesn’t know what a citation actually is.
Can you prove otherwise? 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. An LLM cannot do this. It’s not designed to- there are neural network AI built on similar foundational principles towards divergent goals that can produce remarkable results in terms of data analysis, but not ChatGPT. It doesn’t understand anything, which is why you can repeatedly ask it about a book only to look it up and discover it doesn’t exist.
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.
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?