Earlier quoted context omitted.
it's the biggest model on OpenRouter, even if you exclude free tier usage https://openrouter.ai/state-of-ai
Roleplay is the largest use-case on openrouter.
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GPT-5.2
961–970 of 1001 posts
Re: GPT-5.2
#962In my experience, the best models are already nearly as good as you can be for a large fraction of what I personally use them for, which is basically as a more efficient search engine. The thing that would now make the biggest difference isn't "more intelligence", whatever that might mean, but better grounding. It's still a big issue that the models will make up plausible sounding but wrong or misleading explanations…
I was using an LLM to summarize benchmarks for me, and I realized after awhile it was omitting information that made the algorithm being benchmarked look bad. I'm glad I caught it early, before I went to my peers and was like "look at this amazing algorithm".
It's important not to assume that LLMs are giving you an impartial perspective on any given topic. The perspective you're most likely getting is that of whoever created the most training data related to that topic.
Re: GPT-5.2
#963In my experience, the best models are already nearly as good as you can be for a large fraction of what I personally use them for, which is basically as a more efficient search engine. The thing that would now make the biggest difference isn't "more intelligence", whatever that might mean, but better grounding. It's still a big issue that the models will make up plausible sounding but wrong or misleading explanations…
> It's still a big issue that the models will make up plausible sounding but wrong or misleading explanations for things,
Due to how LLMs are implemented, you are always most likely to get a bogus explanation if you ask for an answer first, and why second.
A useful mental model is: imagine if I presented you with a potential new recruit's complete data (resume, job history, recordings of the job interview, everything) but you only had 1 second to tell me "hired: YES OR NO"
And then, AFTER you answered that, I gave you 50 pages worth of space to tell me why your decision is right. You can't go back on that decision, so all you can do is justify it however you can.
Do you see how this would give radically different outcomes vs. giving you the 50-page scratchpad first to think things through, and then only giving me a YES/NO answer?
Re: GPT-5.2
#964Earlier quoted context omitted.
Problem is that by default ChatGPT has the “Reference chat history” option enabled in the Memory options. This causes any previous conversation to leak into the current one. Just creating a new conversation is not enough, you also need to disable that option.
Only your questions are in it though
Are you sure? What makes you think so?
Re: GPT-5.2
#965Earlier quoted context omitted.
I ask for confidence scores in my custom instructions / prompts, and LLMs do surprisingly well at estimating their own knowledge most of the time.
I’m with the people pushing back on the “confidence scores” framing, but I think the deeper issue is that we’re still stuck in the wrong mental model. It’s tempting to think of a language model as a shallow search engine that happens to output text, but that metaphor doesn’t actually match what’s happening under the hood. A model doesn’t “know” facts or measure uncertainty in a Bayesian sense. All it really does is t…
I mean... That is exactly how our memory works. So in a sense, the factually incorrect information coming from LLM is as reliable as someone telling you things from memory.
Re: GPT-5.2
#966Earlier quoted context omitted.
If somebody asks a question on Stackoverflow, it is unlikely that a human who does not know the answer will take time out of their day to completely fabricate a plausible sounding answer.
People are confidently incorrect all the time. It is very likely that people will make up plausible sounding answers on StackOverflow. You and I have both taken time out of our days to write plausible sounding answers that are essentially opposing hallucinations.
Sites like stackoverflow are inherently peer-reviewed, though; they've got a crowdsourced voting system and comments that accumulate over time. People test the ideas in question.
This whole "people are just as incorrect as LLMs" is a poor argument, because it compares the single human and the single LLM response in a vacuum. When you put enough humans together on the internet you usually get a more meaningful result.
Re: GPT-5.2
#967Earlier quoted context omitted.
> wrong or misleading explanations Exactly the same issue occurs with search. Unfortunately not everybody knows to mistrust AI responses, or have the skills to double-check information.
If somebody asks a question on Stackoverflow, it is unlikely that a human who does not know the answer will take time out of their day to completely fabricate a plausible sounding answer.
Have you ever heard of Dunning Kruger effect?
There's a reason why there are upvotes, solution and third party edit system in StackOverflow - people will spend time to write their "hallucinations" very confidently.
Re: GPT-5.2
#968Earlier quoted context omitted.
LLMs are an especially tough case, because the field of AI had to spend sixty years telling people that real AI was nothing like what you saw in the comics and movies; and now we have real AI that presents pretty much exactly like what you used to see in the comics and movies.
But it cannot think or mean anything, it's just a clever parrot so it's a bit weird. I guess uncanny is the word. I use it as google now, like just to search stuff that are hard to express with keywords.
> clever parrot
Is it irony that you duckspeak this term? Are you a stochastically clever monkey to avoid using the standard cliche?
The thing I find most educating about AI is that it unfortunately mimics the standard of thinking of many humans...
Re: GPT-5.2
#969Earlier quoted context omitted.
I’m with the people pushing back on the “confidence scores” framing, but I think the deeper issue is that we’re still stuck in the wrong mental model. It’s tempting to think of a language model as a shallow search engine that happens to output text, but that metaphor doesn’t actually match what’s happening under the hood. A model doesn’t “know” facts or measure uncertainty in a Bayesian sense. All it really does is t…
I mean... That is exactly how our memory works. So in a sense, the factually incorrect information coming from LLM is as reliable as someone telling you things from memory.
But not really? If you ask me a question about Thai grammar or how to build a jet turbine, I'm going to tell you that I don't have a clue. I have more of a meta-cognitive map of my own manifold of knowledge than an LLM does.
Re: GPT-5.2
#970Earlier quoted context omitted.
I ask for confidence scores in my custom instructions / prompts, and LLMs do surprisingly well at estimating their own knowledge most of the time.
I’m with the people pushing back on the “confidence scores” framing, but I think the deeper issue is that we’re still stuck in the wrong mental model. It’s tempting to think of a language model as a shallow search engine that happens to output text, but that metaphor doesn’t actually match what’s happening under the hood. A model doesn’t “know” facts or measure uncertainty in a Bayesian sense. All it really does is t…
>A model doesn’t “know” facts or measure uncertainty in a Bayesian sense. All it really does is traverse a high‑dimensional statistical manifold of language usage, trying to produce the most plausible continuation.
And is that that different than what we do under the scenes? Is there a difference between an actual fact vs some false information stored in our brain? Or both have the same representation in some kind of high‑dimensional statistical manifold in our brains, and we also "try to produce the most plausible continuation" using them?
There might be one major difference is at a different level: what we're fed (read, see, hear, etc) we also evaluate before storing. Does LLM training do that, beyond some kind of manually assigned crude "confidence tiers" applied to input material during training (e.g. trust Wikipedia more than Reddit threads)?