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Notes on OpenAI's new o1 chain-of-thought models

simonwillison.net

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Re: Notes on OpenAI's new o1 chain-of-thought models

#111

Post by anon291 »

pedrosorio wrote: post #110
MPSimmons wrote: post #106

Earlier quoted context omitted.

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…

> It knows english at or above a level equal to most fluent speakers, and it also can produce output that is not just a likely output, but is a logical output This is not an apt description of the system that insists the doctor is the mother of the boy involved in a car accident when elementary understanding of English and very little logic show that answer to be obviously wrong. https://x.com/colin_fraser/status/183…

Keep in mind that the system always chooses randomly so there is always a possibility it commits to the wrong output.

I don't know why openAi won't allow determinism but it doesn't, even with temperature set to zero

Re: Notes on OpenAI's new o1 chain-of-thought models

#112

Post by ramraj07 »

pedrosorio wrote: post #110
MPSimmons wrote: post #106

Earlier quoted context omitted.

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…

> It knows english at or above a level equal to most fluent speakers, and it also can produce output that is not just a likely output, but is a logical output This is not an apt description of the system that insists the doctor is the mother of the boy involved in a car accident when elementary understanding of English and very little logic show that answer to be obviously wrong. https://x.com/colin_fraser/status/183…

Many of my PhD and post doc colleagues who emigrated from Korea, China and India who didn’t have English as the medium of instruction would struggle with this question. They only recover when you give them a hint. They’re some of the smartest people in general. If you try to stop stumping these models with trick questions and ask it straightforward reasoning systems it is extremely performant (O1 is definitely a step up though not revolutionary in my testing).

Re: Notes on OpenAI's new o1 chain-of-thought models

#113

Post by tjoff »

MPSimmons wrote: post #106
layer8 wrote: post #18

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…

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…

And how much data can you give it?

I'm not up to date with these things because I haven't found them useful. But with what you said, and previous limitations in how much data they can retain essentially makes them pretty darn useless for that task.

Great learning tool on common subjects you don't know, such as learning a new programming-language. Also great for inspiration etc. But that's pretty much it?

Don't get me wrong, that is mindblowingly impressive but at the same time, for the tasks in front of me it has just been a distracting toy wasting my time.

Re: Notes on OpenAI's new o1 chain-of-thought models

#114

Post by throwaway314155 »

ldjkfkdsjnv wrote: post #15
OutOfHere wrote: post #12

Earlier quoted context omitted.

> A properly written requirements spec, and an engineer, can do the work of 5. I do not think this will scale. GPT o1 is presumably good for bootstrapping a project using tools that the engineer is not familiar with. The model will struggle to update a sizable codebase, however, with dependencies between the files. Secondly, no matter the size of the codebase and no matter the model used, the engineer still has to re…

I respectfully, but completely disagree. Right now with sonnet 3.5 + cursor ide, I'm not writing that much of my own code at my FAANG job. I am generating a ton, passing in documentation from internal libraries, iterating on the result. Most of the time, I just accept its changes. This is going to rapidly happen. All we need are a few more model releases, not even a step function improvement

[flagged]

Re: Notes on OpenAI's new o1 chain-of-thought models

#115

Post by minimaxir »

throwaway314155 wrote: post #109
henry2023 wrote: post #103

>> 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…

> So IMO level right? What?

In this case, IMO means International Mathematical Olympiad

Re: Notes on OpenAI's new o1 chain-of-thought models

#116

Post by Terr_ »

layer8 wrote: post #23
tptacek wrote: post #20

Earlier quoted context omitted.

I've had the opposite experience with some coding samples. After reading Nick Carlini's post, I've gotten into the habit of powering through coding problems with GPT (where previously I'd just laugh and immediately give up) by just presenting it the errors in its code and asking it to fix them. o1 seems to be effectively screening for some of those errors (I assume it's just some, but I've noticed that the o1 things…

My experience is likely colored by the fact that I tend to turn to LLMs for problems I have trouble solving by myself. I typically don't use them for the low-hanging fruits. That's the frustrating thing. LLMs don't materially reduce the set of problems where I'm running against a wall or have trouble finding information.

LLMs: When the code can be made by an enthusiastic new intern with web-search and copy-paste skills, and no ability to improve under mentorship. :p

Tangentially related, a comic on them: https://existentialcomics.com/comic/557

Re: Notes on OpenAI's new o1 chain-of-thought models

#117

Post by halJordan »

OutOfHere wrote: post #37
jumploops wrote: post #3

> the idea that I can run a complex prompt and have key details of how that prompt was evaluated hidden from me feels like a big step backwards. As a developer, this is highly concerning, as it makes it much harder to debug where/how the “reasoning” went wrong. The pricing is also silly, because I’m paying for tokens I can’t see. As a user, I don’t really care. LLMs are already magic boxes and I usually only care abo…

> As a user, I don’t really care. Tell me: Just how is it fair for a user to pay for the reasoning tokens without actually seeing them? If they are not shared, the service can bill you anything they want for them!

Yeah it is fair. You don't pay a lawyer for 40s of work expecting to see all the research between your consult and the document. You don't pay a cook for a meal and expect to sit and interrogate all the ingredients and the oven temperature.

Re: Notes on OpenAI's new o1 chain-of-thought models

#118

Post by kylebenzle »

layer8 wrote: post #18

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…

> having no way to assess if what it conjures up from its weights is factual or not.

This comment makes no sense in the context of what an LLM is. To even say such a thing demonstates a lack of understandting of the domain. What we are doing here is TEXT COMPLETION, no one EVER said anything about being accurate and "true". We are building models that can complete text, what did you think an LLM was, a "truth machine"?

Re: Notes on OpenAI's new o1 chain-of-thought models

#119

Post by kylebenzle »

MPSimmons wrote: post #106
layer8 wrote: post #18

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…

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…

Perfectly well put! We should change the name from "AI" (which it is not) to something like, "lossy compressed databases".

Re: Notes on OpenAI's new o1 chain-of-thought models

#120

Post by poikroequ »

la64710 wrote: post #79

Please please please stop saying thought. This has nothing to do with the word thought. When we say the word thought it means something. Please don’t use the same word for whatever AI is doing and trivialize the word. Invent a new word if needed but for Pete’s sake be accurate and truthful.

It's called terminology. Every field has words that mean very different things from the layman's definition. It's nothing to get upset about.
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