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30% drop in O1-preview accuracy when Putnam problems are slightly variated

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Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#411

Post by HenriTEL »

themgt wrote: post #375
jerf wrote: post #197

I remember when this stuff was all coming out and people were finally excited about ChatGPT getting the problem with "which is heavier, a 10 pound bag of feathers or a 10 pound bag of bricks?" problem correct. But of course it got it correct. It was in the training set. Vary the problem slightly by just changing the nouns, or changing the numbers so that one in fact was heavier than the other, and performance went al…

I just asked Claude 3.5 Sonnet, which appears to have improved its response with CoT but there's mistakes that demonstrate the model doesn't really "understand": Q: 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? C: Let me think about this step by step: A woman…

I must be to tired as I can't find any flaw in that reasoning.

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#412

Post by _heimdall »

ben_w wrote: post #152
_heimdall wrote: post #125

Earlier quoted context omitted.

> The goal is to make it intelligent, by which OpenAI in particular explicitly mean "economically useful", not simply to be shiny I never understood why this definition isn't a huge red flag for most people. The idea of boiling what intelligence is down to economic value is terrible, and inaccurate, in my opinion.

Everyone has a very different idea of what the word "intelligence" means; this definition has got the advantage that, unlike when various different AI became superhuman at arithmetic, symbolic logic, chess, jeopardy, go, poker, number of languages it could communicate in fluently, etc., it's tied to tasks people will continuously pay literally tens of trillions of dollars each year for because they want those tasks d…

This definition alone might be fine enough if the word "intelligence" wasn't already widely used outside of AI research. It is though, and the idea that intelligence is measured solely through economic value is a very, very strange approach.

Try applying that definition to humans and you pretty quickly run into issues, both moral and practical. It also invalidates basically anything we've done over centuries considering what intelligence is and how to measure it.

I don't see any problem at all using economic value as a metric for LLMs or possible AIs, it just needs a different term than intelligence. It pretty clearly feels like for-profit businesses shoehorning potentially valuable ML tools into science fiction AI.

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#413

Post by mu53 »

stocknoob wrote: post #388
NewsaHackO wrote: post #211

Earlier quoted context omitted.

This happens literally every time. Someone always says "ChatGPT can't do this!", but then when someone actually runs the example, chatGPT gets it right. Now what the OP is going to do next is proceed to move goalposts and say like "but umm I just asked chatgpt this, so clearly they modified the code in realtime to get the answer right"

Similarly, in every thread there’s an AI skeptic who says LLMs are “useless” for coding, and never provides an example query for what they were trying.

If you ask about more niche language features or libraries, chatgpt will make up libraries or functions to fill the gap.

When asking an LLM to write a script for you, I would say 10 to 30 % of the time that it completely fails. Again, making up an API or just getting things straight up wrong.

Its very helpful, especially when starting from 0 with the beginner questions, but it fails in many scenarios.

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#414

Post by jorvi »

ipaddr wrote: post #351
jorvi wrote: post #332

Earlier quoted context omitted.

Prompt: In the Netherlands, in terms of drinks, is there a particular spirit that represents the country? > Yes, in the Netherlands, jenever (also known as genever) is the traditional spirit that represents the country. Jenever is a type of Dutch gin that has a distinctive flavor, often made from malt wine and flavored with juniper berries. It has a long history in the Netherlands, dating back to the 16th century, an…

So you believe they are incorrect because regionally some area would select something different because it represented that area. But your question asked nationally.. is there a better answer than the one they gave? Were you expecting a no?

See my more detailed upthread response here: https://news.ycombinator.com/item?id=42569937

But, like Zahlman points out, its a trick question, and instead of admitting it doesn't know or even prepending "I don't know for sure, but:", it just burps up its best-effort answer. There is no one spirit that represents The Netherlands. If a LLM is so good it "oneshots any question", it should realize it doesn't have a unanimous answer and tell me.

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#415

Post by tomlockwood »

One I just did:

Q: I was heading to Aberdeen from London. On my way I passed seven wives, each wife had seven sacks, and in each sack there were seven cats and each cat had seven fish. How many were going to London?

A: This riddle is a play on words, and the answer is hidden in the phrasing! You mentioned you were heading to Aberdeen from London, but you didn’t say anything about the seven wives, their sacks, cats, or fish actually being headed to London.

The only one going to London is you.

So the answer is: 1 person (you) are going to London.

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#417

Post by [deleted] »

Isinlor wrote: post #395
jdietrich wrote: post #356

Earlier quoted context omitted.

I've just tested a number of permutations with Claude 3.5 Sonnet. It correctly answered all variants I tried on the first attempt, as follows: Which is heavier, a 9.99 kilogram tungsten cube or a 10.01 kilogram block of aerogel? Which is heavier, 10,000 steel balls weighing 0.999 grams each or 10,000 polystyrene balls weighing 1.001 grams each? Which is heavier, a 10.01kg block of steel on Venus or a 9.99kg bag of fe…

BTW - the model may be wrong depending on the example. More voluminous objects displace more air and due to buoyancy are lighter for the same mass. The proper way to ask it would be to ask which object has more mass.

[deleted]

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#418

Post by tomlockwood »

ttul wrote: post #352
s1mplicissimus wrote: post #169

Earlier quoted context omitted.

> 1. OpenAI has confirmed it’s not in their train (unlike putnam where they have never made any such claims) Companies claim lots of things when it's in their best financial interest to spread that message. Unfortunately history has shown that in public communications, financial interest almost always trumps truth (pick whichever $gate you are aware of for convenience, i'll go with Dieselgate for a specific example).…

OpenAI’s credibility is central to its business: overstating capabilities risks public blowback, loss of trust, and regulatory scrutiny. As a result, it is unlikely that OpenAI would knowingly lie about its models. They have much stronger incentives to be as accurate as possible—maintaining their reputation and trust from users, researchers, and investors—than to overstate capabilities for a short-term gain that woul…

OpenAI's apparent credibility is central to their business.

They're spruiking a 93rd percentile performance on the 2024 International Olympiad in Informatics with 10 hours of processing and 10,000 submissions per question.

Like many startups they're still a machine built to market itself.

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#419

Post by Isinlor »

frikskit wrote: post #290

An interesting example of this is: There are 6 “a”s in the sentence: “How many ‘a’ in this sentence?” https://chatgpt.com/share/677582a9-45fc-8003-8114-edd2e6efa2... Whereas the typical “strawberry” variant is now correct. There are 3 “r”s in the word “strawberry.” Clearly the lesson wasn’t learned, the model was just trained on people highlighting this failure case.

Transformers are very bad at counting due to how their internals work. But if you ask them to use explicit counter the problem disappears:

https://chatgpt.com/share/6775c9a6-8cec-8007-b709-3431e7a2b2...

Basically one feed forward is not Turing complete, but autoregressive (feeding previous output back into itself) are Turing complete.

Re: 30% drop in O1-preview accuracy when Putnam problems are slightly variated

#420

Post by themgt »

HenriTEL wrote: post #411
themgt wrote: post #375

Earlier quoted context omitted.

I just asked Claude 3.5 Sonnet, which appears to have improved its response with CoT but there's mistakes that demonstrate the model doesn't really "understand": Q: 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? C: Let me think about this step by step: A woman…

I must be to tired as I can't find any flaw in that reasoning.

The joke/riddle text is "he says" but Claude says "their son" and suggests the doctor might be a woman.

More substantively: "This is a classic riddle that highlights gender bias - many people assume doctors must be men, but don't initially consider that the doctor could be the father." is totally nonsensical.

The text is a gender (and meaning) inversion of the classic riddle to confuse LLMs. Even though Claude correctly CoTs that the doctor is the father, it seemingly then copypastas the explanation to the original riddle but swaps the gender, completely missing the point.

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