221–230 of 659 posts
The o1-preview model still hallucinates non-existing libraries and functions for me, and is quickly wrong about facts that aren't well-represented on the web. It's the usual string of "You're absolutely correct, and I apologize for the oversight in my previous response. [Let me make another guess.]" While the reasoning may have been improved, this doesn't solve the problem of the model having no way to assess if what…
>> which is larger 3.9, 3.10 or 3.11 To determine which number is larger among 3.9, 3.10, and 3.11, let's compare Standard Decimal Interpretation: 3.9 is simply 3.9. 3.10 is equivalent to 3.1 because trailing zeros after the decimal point do not add value in decimal numbers. 3.11 remains 3.11. Comparison: 3.9 vs. 3.1 vs. 3.11 3.11 is the largest number. Version Number Interpretation: In version numbering, each segmen…
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What I'm not able to comprehend is why people are not seeing the answer as brilliant! Any ordinary mortal (like me) would have jumped to the conclusion that answer is "Father" and would have walked away patting on my back, without realising that I was biased by statistics. Whereas o1, at the very outset smelled out that it is a riddle - why would anyone out of blue ask such question. So, it started its chain of thoug…
The 'riddle': A woman and her son are in a car accident. The woman is sadly killed. The boy is rushed to hospital. When the doctor sees the boy he says "I can't operate on this child, he is my son". How is this possible? GPT Answer: The doctor is the boy's mother Real Answer: Boy = Son, Woman = Mother (and her son), Doctor = Father (he says...he is my son) This is not in fact a riddle (though presented as one) and th…
There is no indication of the sex of the doctor, and families that consist of two mothers do actually exist and probably doesn't even count as that unusual.
The o1-preview model still hallucinates non-existing libraries and functions for me, and is quickly wrong about facts that aren't well-represented on the web. It's the usual string of "You're absolutely correct, and I apologize for the oversight in my previous response. [Let me make another guess.]" While the reasoning may have been improved, this doesn't solve the problem of the model having no way to assess if what…
I honestly can’t believe this is the hyped up “strawberry” everyone was claiming is pretty much AGI. Senior employees leaving due to its powers being so extreme I’m in the “probabilistic token generators aren’t intelligence” camp so I don’t actually believe in AGI, but I’ll be honest the never ending rumors / chatter almost got to me Remember, this is the model some media outlet reported recently that is so powerful…
it seems trivial, but I tried for more than 2 hours in the past to get gpt4 to play tic-tac-toe optimally and failed (CoT prompt,etc.). The result were too many illegal moves and absolutely no optimal strategy.
o1-preview can do it really well [1]
However, when I use a non-standard grid (3x5) it fails to play optimally. But it makes legal moves and it recognized I had won. [2]
My conclusion at the time was that either "spatial reasoning" doesn't work and/or planning is needed. Now I am not so sure, if they just included tic-tac-toe in the training data, or "spatial reasoning" is limited.
[1] https://chatgpt.com/share/e/66e3e784-26d4-8013-889b-f56a7fed... [2] https://chatgpt.com/share/e/66e3eae0-2d38-8013-b900-50e6f792...
Perhaps the system prompt is part of the magic?
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The failure is in how you're using it. I don't mean this as a personal attack, but more to shed light on what's happening. A lot of people use LLMs as a search engine. It makes sense - it's basically a lossy compressed database of everything its ever read, and it generates output that is statistically likely - varying degrees of likeliness depending on the temperature, as well as how many times the particular weights…
> Treat it as a naive but intelligent intern That’s the problem: it’s a _terrible_ intern. A good intern will ask clarifying questions, tell me “I don’t know” or “I’m not sure I did it right”. LLMs do none of that, they will take whatever you ask and give a reasonable-sounding output that might be anything between brilliant and nonsense. With an intern, I don’t need to measure how good my prompting is, we’ll usually…
Your expectations are bigger than mine
(Though some will get stuck in "clarifying questions" and helplessness and not proceed neither)
Earlier quoted context omitted.
The 'riddle': A woman and her son are in a car accident. The woman is sadly killed. The boy is rushed to hospital. When the doctor sees the boy he says "I can't operate on this child, he is my son". How is this possible? GPT Answer: The doctor is the boy's mother Real Answer: Boy = Son, Woman = Mother (and her son), Doctor = Father (he says...he is my son) This is not in fact a riddle (though presented as one) and th…
I mean, it's entirely possible the boy has two mothers. This seems like a perfectly reasonable answer from the model, no?
From the article: > I expect to continue mostly using GPT-4o (and Claude 3.5 Sonnet) I saw similar comments elsewhere and I'm stunned - am I the only one who considers 4o a step back when compared to 4 for textual input and output? It basically gives fast semi-useful answers that seem like a slightly improved 3.5.
It's poor performance on benchmarks drives my skepticism of LLM benchmarking in general. I trust my feel for the models much more, and my feel was that 0314 was great.
The one thing that 0314 doesn't do well are the tricks like structured output and tool calling which makes it a less useful agentic type of tool, but from a pure thinking perspective, I think it's the best.