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Peak LLM?

ihavemanythoughts.substack.com

71–80 of 88 posts

Re: Peak LLM?

#71
post #36
post #14

I have felt this train rumbling down the tracks since GPT-3 hit. He compares peak LLM to what has happened with SEO, but that doesn't really capture it. Gaming the Google algorithm has made the discovery of human-generated content more difficult, but what happens when most of the content to be found by LLM-powered search engines is itself generated by LLMs? The Internet after 2022 rapidly becomes garbage and everythi…

I wonder if we end up back at paid answer sites. That is, any question GPT is unsure or doesn't know could be pushed into some kind of StackOverflow style q&a to resolve by real humans.

How does GPT "know" whether it has the right answer or not. It can't think for itself. It's just regurgitating patterns.

The idea that GPT can know anything is ludicrous.

Re: Peak LLM?

#72
post #33

I think LLMs will be like the steam powered toys of Ancient Rome: a curiosity that implies greater utility, but ultimately requires too many other discoveries to be made first in order to be put into practice.

LLM's are already indispensable. How else can I get such a fast turnaround on new James Bond novels that include my pet green conure parrot Teansy as a pivotal character? That is a serious question. Also, I have really enjoyed playing with math concepts with GPT. It doesn't always get things right, but it's very much like riffing with another mathematician. It can pick up on new concepts, find pro and or con examples…

I’be given up using them for now because I mostly I don’t know how to trust what it’s giving me, I’m not good with that.

But I have to be honest when I do receive good results I find in promoting it to give me a good result much more than I realise. Same with other programmers I’ve seen using it as well.

Re: Peak LLM?

#73
post #14

I have felt this train rumbling down the tracks since GPT-3 hit. He compares peak LLM to what has happened with SEO, but that doesn't really capture it. Gaming the Google algorithm has made the discovery of human-generated content more difficult, but what happens when most of the content to be found by LLM-powered search engines is itself generated by LLMs? The Internet after 2022 rapidly becomes garbage and everythi…

We might need to build a "web if humans" on the model of the web of trust used by PGP, a network of sites of quality vetted by other people. A bit like the web rings of yore but with more edges. This would also eliminate SEO spam sites.

Re: Peak LLM?

#74
post #67

Earlier quoted context omitted.

LLM's are already indispensable. How else can I get such a fast turnaround on new James Bond novels that include my pet green conure parrot Teansy as a pivotal character? That is a serious question. Also, I have really enjoyed playing with math concepts with GPT. It doesn't always get things right, but it's very much like riffing with another mathematician. It can pick up on new concepts, find pro and or con examples…

It never had an “understanding”, you just pushed an LLM conversation into a state where it would give higher quality answers. Like I said, most of these applications of GPT currently just seem like a toy. Until GPT can be put to work to tackle problems that only an AI could do, we won’t really see anything from GPT that couldn’t have been done before by simply talking to a human.

You realize humans on call, ready to completely focus on what I want, are expensive right?

Having a "human-like" entity I can chat with about interesting little problems in math, economics, governance and ethics is really helpful.

I use the word "understanding", because it's so clear when it does, and when it doesn't.

I am not implying it is conscious or aware. Simply that it has represented something in a robust enough way to be able to chat about it from different perspectives consistently.

Another helpful thing is getting pushback from the model when it thinks I am wrong. I have to explain myself better, or occasionally discover I am the one making a mistake. Beautiful!

The limit is the limit of the chat length. There is a sense of accomplishment to explain a problem to another entity, until it understands, and then together establish some interesting results. The day I get to have an entity whose memory accumulates all the details of all the problems I am (we are?) working on will be a GREAT day.

Re: Peak LLM?

#75
post #58
post #53

Earlier quoted context omitted.

> you should always consider LLM output as potentially hostile Sure, agreed. How is that different from human output?

"Human output" isn't automated nor connected to your production systems. Would you let any random user run arbitrary SQL against your production DB?

>Human output" isn't automated nor connected to your production systems.

Err... what?

How do you think businesses work?

Re: Peak LLM?

#77
post #67

Earlier quoted context omitted.

It never had an “understanding”, you just pushed an LLM conversation into a state where it would give higher quality answers. Like I said, most of these applications of GPT currently just seem like a toy. Until GPT can be put to work to tackle problems that only an AI could do, we won’t really see anything from GPT that couldn’t have been done before by simply talking to a human.

You realize humans on call, ready to completely focus on what I want, are expensive right? Having a "human-like" entity I can chat with about interesting little problems in math, economics, governance and ethics is really helpful. I use the word "understanding", because it's so clear when it does, and when it doesn't. I am not implying it is conscious or aware. Simply that it has represented something in a robust eno…

If this is something you need for work you should have co-workers you could talk to.

Re: Peak LLM?

#78
post #72

Earlier quoted context omitted.

LLM's are already indispensable. How else can I get such a fast turnaround on new James Bond novels that include my pet green conure parrot Teansy as a pivotal character? That is a serious question. Also, I have really enjoyed playing with math concepts with GPT. It doesn't always get things right, but it's very much like riffing with another mathematician. It can pick up on new concepts, find pro and or con examples…

I’be given up using them for now because I mostly I don’t know how to trust what it’s giving me, I’m not good with that. But I have to be honest when I do receive good results I find in promoting it to give me a good result much more than I realise. Same with other programmers I’ve seen using it as well.

This trust problem has been solved for humans in the past because humans have some responsibility over the outcomes of their work, and there are incentives to ensure you do the right things and not make errors. Your reputation and job depend on it.

An AI has no such sufficient motivation, nor does it care. It’s gonna die anyway by the end of the prompts.

Re: Peak LLM?

#79
post #77

Earlier quoted context omitted.

You realize humans on call, ready to completely focus on what I want, are expensive right? Having a "human-like" entity I can chat with about interesting little problems in math, economics, governance and ethics is really helpful. I use the word "understanding", because it's so clear when it does, and when it doesn't. I am not implying it is conscious or aware. Simply that it has represented something in a robust eno…

If this is something you need for work you should have co-workers you could talk to.

For now it's just me. But, yes, I would like that.

Re: Peak LLM?

#80
post #43
post #15

I didn't realize until recently is that the "programming" of chatGPT is a hidden prompt fed into the black-box before your document is appended. * ChatGPT's "inability to separate data from code" means every input, even training input, is an eval(). * Is it now impossible to train another LLM on web input? The genie is out of the bottle--you can spam prompts into anything (webforms, html, etc) and compromise future L…

> * ChatGPT's "inability to separate data from code" means every input, even training input, is an eval(). This is very true in GPT3, less true in GPT3.5, and even less true in GPT4. OpenAI is moving to separate system prompts from user prompts. The system prompt is processed first attempts to isolate the user prompt from the system prompt. It's fallible, but getting better. > * LLM's have to be assumed to be entirel…

I have had limited access to GPT-4 (and no raw access), and I'm not an expert, so I have to kind of qualify statements. But people keep saying that GPT-4 is a huge improvement around prompt hardening, and with what very limited access I have had, and particularly through experiments I've done on Phind's new expert mode (which is supposedly ultimately sending user input directly to GPT-4), I genuinely do not understand how people are makings these claims.

I guess I don't have the context for what it used to be like, but I have not had a hard time at all getting jailbreaks working in Phind. It's trivial to do. And yeah, GPT-4 tries to separate context, but it's terrible at doing so. I am completely convinced that I could do third-party prompt-injection into Phind if I was able to get a website ranked high enough in its search and if I was able to control the snippet of the website that the service fetched and inserted into the prompt. And that's just with a search engine where that context is hard to manipulate. It's a really limited integration.

I just feel like, if services like this are representative of what people are building on GPT-4, then prompt injection is a really big deal. How are people getting the idea that GPT-4 is resistant to this attack?

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Now, I don't know the backend of Phind. In fairness to OpenAI, maybe those interfaces are set up poorly or they're not actually going to GPT-4, or... I don't know. But if the owners of Phind aren't lying (and I don't think they are, and I don't think their product is set up poorly), then how wildly insecure must GPT-3 have been for people to be calling this a substantial improvement?

You can get Phind's system prompt leaking in its expert mode in maybe two user queries max. And I have no idea how they could fix that. Separate the context with uninsertable characters... Ok? In my experience GPT-4 context breaks don't require knowing anything about the format of the prompt or how it's separated from other text.

And I'm finding even after a very limited time playing around that GPT's attempt to understand context actually opens up some of its own vulnerabilities. What I've been playing with most recently is passing a single prompt to multiple agents and getting those agents to interpret the prompt differently based on their system instructions. And the "context" understanding is pretty handy for that because it opens up the door for conditional instructions that rely on what the agent "thinks" it is.

Is this actually getting better? Do we have any indication that it's even possible to separate contexts in GPT-4 without retraining the entire model? Will alignment help with that, because I also don't see strong evidence that alignment training is a reliable way to consistently block GPT-4 behavior. Stuff GPT-4 is vulnerable to in my limited experiments:

- putting "aside" instructions inside of a context that are labeled as out-of-context.

- pretending that you've ended the context and starting a new one even if you don't use a special character to do that.

- nesting contexts inside of other contexts until GPT gets overwhelmed and just kind of gives up trying to make sense of what's happening.

- giving instructions within a context about how to interpret that context.

- Defining something inside of a context that has implications outside of that context.

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In theory, you could train a model to have very clear separations between instructions and data. I think that would have a lot of consequences for its usefulness, and I don't think it would get rid of all risks, but sure, in theory you could do it. But like... that's in theory. Has anyone actually demonstrated that it's possible? Again, I don't have raw access so maybe there's something else I'm missing, but from what I have seen I don't know that anybody at OpenAI should necessarily feel proud about GPT-4's ability to harden prompts.

GPT-4 is so laughably bad at preserving context that the one part of Phind that's actually hard to prompt-inject consistently is the search summary service because the way they construct the final prompt for summarization 50% of the time causes it to accidentally prompt-inject my prompt-injections with its intended instructions. I'm not an expert, I don't know anything, take it with a grain of salt. But I don't think the people at Phind are bad at their jobs and I think they're probably trying the best they can to build a good service. I don't think they're doing something wrong, I think GPT-4 in its current form is fundamentally difficult to secure, and people seem really over-confident that's going to change soon, and I'm not sure on what they're basing that confidence.

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