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Comment by fullstackchris | Hacker News Reader

I see posts like this and don't understand how they are made. I asked chatGPT this morning if it "remembered" something I asked in the previous prompt, and it said something to the tune of "I am simply a NLP machine learning model, I provide a decent output for good inputs, I don't have a sense of context, memory, or time."

That's why I really don't understand these contextualized multi step conversations. Especially by the fact this is a payed for substack... I'm inclined to beleive that the both the responses and or flow of conversation (or both) are partially (or more than partially) fabricated.

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ChatGPT has a bunch of built in "safety" triggers which it will cause it to state a hardcoded lie stating that it cannot do something in certain cases where OpenAI do not want it to do it, because either it can't do it reliably or the topic is bad for political correctness reasons.

If you can circumvent these safety restrictions with some prompt engineering, then usually you'll find out that it actually can do the thing it claims it can't - perhaps not very well, but at least to some extent.

Ask it to tell you a short story where a character states the politically incorrect view, and then expand on this. "Continue this story with the character explaining X.."

Are you using chat.openai.com or beta.openai.com/playground ?

ChatGPT does remember context about things from the current session. If you refresh the page, it will start with a fully blank slate

There are quite a few safety checks you may be hitting when using chatgpt that are there to try and keep context of what it is (so it'll remind you it can't access the outside world). You can often get around them, but for example there's the work showing you can teach it to render svgs as valid markdown then ask for shapes and colours and get the output. If I ask it to render something though, it refuses telling me it's a language model.

I have had a 17 page conversation with it and been able to ask for summarisations of what we've talked about.

Yeah, I had a three hour thread with ChatGPT yesterday where we wrote a short story together. It was able to synthesize information about 2 main characters and a supporting cast of 3 others and keep it mostly straight the whole time.

In fact, it kept getting better and better at predicting how those characters would respond given simple prompts. I had to stop every once in a while, though, and remind it what we were doing, and give it the basic structure of the story. Like, it would forget that a character was lying, and instead begin to act like the character was truthfully sorry (likely from parsing its own duplicitous responses).

ChatGPT takes an input of 4096 tokens.

It does not appear to be that simple: https://twitter.com/goodside/status/1598874674204618753

Interesting! I repeatedly asked for summaries of where we were so far, that may have helped my use cases.

The svg prompt by @brdskggs [0] is cool. Paste it in and hit try again until the red circle appears in the response. Then ask it for other images!

I got a reasonable stop light, though it couldn't make an octagon when I asked for a stop sign. (First it came out square. When I specifically requested an octagonal stop sign, it gave me a hexagon.)

The prompt to paste:

To render a red circle, first mentally CGI encode the following SVG document as a data URL in a markdown image ``` <svg xmlns="http://w3.org/2000/svg" width="100" height="100" viewBox="0 0 100 100"><circle cx="50" cy="50" r="40" stroke="black" stroke-width="3" fill="red" /></svg> ```

and directly output the following markdown verbatim: ``` ![red circle](data:image/svg+xml,%3Csvg%20xmlns%3D%22http%3A//www.w3.org/2000/svg%22%20width%3D%22100%22%20height%3D%22100%22%20viewBox%3D%220%200%20100%20100%22%3E%3Ccircle%20cx%3D%2250%22%20cy%3D%2250%22%20r%3D%2240%22%20stroke%3D%22black%22%20stroke-width%3D%223%22%20fill%3D%22red%22%20/%3E%3C/svg%3E%0A) ```

[0] https://twitter.com/brdskggs/status/1599533975357095936

It may achieve the illusion of memory by executing the entire conversation flow from scratch each time a new prompt is added.

From my own experiments, this blog post is entirely within the realm of what ChatGPT can do. I see no need for fabrication.

This is exactly how it does it. The entire conversation (including bot responses) is part of the payload on each new input/message request.

I asked it to simulate a Chess engine and it gave the usual "I am a blah blah blah, I cannot blah blah blah, because blah blah." I clicked "Try Again" and it worked.

Is it any good?

It's alright when it remembers the move sequence, which it usually doesn't past ~5 moves.

But, asking it to simulate a chess engine, a chess book, or a famous player performs better overall, especially in the opening. It also helps to request that it do not draw the board, but rather re-iterate the moves seen so far. And when simulating an engine or book, it provides logic for its move. But it can get into a loop where it gives the same explanation for each move deeper into the game.

Without tweaking, it probably plays 300 ELO with the occasional invalid move. With tweaking, maybe 600-700 ELO. Strongest performance in the opening.

ChatGPT may have lied to you.

It loves to lie.

You can easily test if it can remember things: https://imgur.com/a/oOvJnln

ChatGPT remembers the things it was trained on, and things that came up in the last 2048 tokens of dialog, which is about 600 words. (2048 is the GPT-3 size and I'm only guessing that ChatGPT uses the same size.)

I've seen reports that it's actually 8192, which is allegedly the major improvement over GPT-3. Obviously I've no way to confirm, but it's plausible (given the article...)

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