https://chatgpt.com/share/67756897-8974-8010-a0e0-c9e3b3e91f...
so far o1-mini has bodied every task people are saying LLMs can’t do in this thread
so far o1-mini has bodied every task people are saying LLMs can’t do in this thread
I'm not at all surprised that it will nondeterministically get it correct sometimes. But if it doesn't get it correct every time, it doesn't "know".
(In fact "going shopping" for errors would still even be fair. It should be correct all the time if it "knows". But it would be different if I was fishing over and over and over and finally got one, versus the first time I asked.)
Edit: It appears it isn't the model I used. The point holds, though, you need to make sure you're off the training set for it to matter. This isn't a "ChatGPT can't do that" post as some are saying, it's more a "you aren't asking what you think you're asking" post.
You get the same problem in a human context in things like code interviews. If you ask an interviewee the exact question "how do you traverse a binary tree in a depth-first manner", you aren't really learning much about the interviewee. It's a bad interview question. You need to get at least a bit off the beaten trail to do any sort of real analysis.
perhaps you stumbled on a rarer case but reading the logs you posted this sounds more like a 4o model than an o1 because it’s doing its thinking in the chat itself plus the procedure you described would probably get you 4o-mini
Could you try playing with the exact numbers and/or substances?
Which is heavier: an 11kg bag of lint or a 20lb bag of gold?
https://chatgpt.com/share/67757720-3c7c-8010-a3e9-ce66fb9f17...
e: cool, this gets downvoted
> While the lint bag is heavier in terms of weight, it's worth mentioning that gold is significantly more valuable per pound compared to lint. This means that even though the lint bag weighs more, the gold bag holds much greater monetary value.
>> A liter of bricks and a liter of feathers both weigh the same—1 kilogram—since they each have a volume of 1 liter. However, bricks are much denser than feathers, so the bricks will take up much less space compared to the large volume of feathers needed to make up 1 liter. The difference is in how compactly the materials are packed, but in terms of weight, they are identical.
> give me a query and i’ll ask it
Here's a query similar to one that I gave to Google Gemini (version unknown), which failed miserably:
---query---
Steeleye Span's version of the old broadsheet ballad "The Victory" begins the final verse with these lines:
Here's success unto the Victory / and crew of noble fame
and glory to the captain / bold Nelson was his name
What does the singer mean by these lines?
---end query---
Italicization is for the benefit of HN; I left that out of my prompt.
of course, it is a sardonic, implicit critique of Admiral Nelson/the victory, etc. but i do think it is a bit subtle.
…but to your point, no idea if the artist intended some more obscure reference.
o1-pro was also able to produce a relatively complete version of original source, though, amusingly, referred to it as a ‘broadside’ rather than ‘broadsheet’. Appropriate given the context!
No, but the answer is obviously wrong. Very, very wrong. The lines are not praise. The song's view of the Victory, and of the battle of Trafalgar, is overwhelmingly negative.
> o1-pro was also able to produce a relatively complete version of original source
...as should be clear, if you read the original source. (Or, heck, just the two lines following the ones in the question.) Being able to reproduce the source makes the error look that much worse.
> though, amusingly, referred to it as a ‘broadside’ rather than ‘broadsheet’.
Both terms are used. https://en.wikipedia.org/wiki/Broadside_ballad :
>> A broadside (also known as a broadsheet) is a single sheet of inexpensive paper printed on one side, often with a ballad, rhyme, news and sometimes with woodcut illustrations.
These lines celebrate the victory of the British ship HMS Victory, led by the famous Admiral Lord Nelson, in the Battle of Trafalgar in 1805.
"Here's success unto the Victory": This line directly praises the ship itself, acknowledging its role in the successful battle. "and crew of noble fame": This recognizes the bravery and skill of the sailors who served aboard the Victory. "and glory to the captain": This line specifically honors Admiral Nelson, the captain of the Victory, for his leadership and strategic brilliance in the battle. "bold Nelson was his name": This emphasizes Nelson's courage and daring, which were legendary. The lines express admiration for the ship, its crew, and most importantly, Admiral Nelson, who became a national hero in Britain for his victory at Trafalgar.
What do you think the singer in "Friends in Low Places" meant in the toast he gave after crashing his ex-girlfriend's wedding?
And I saw the surprise and the fear in his eyes
when I took his glass of champagne
and I toasted you, said "Honey, we may be through
but you'll never hear me complain"
But I think specifying that the singer has crashed his ex-girlfriend's wedding is already enough that you deserve to fail if your answer is "he says he's not upset, so what he means is that he's not upset". It's not any kind of leap to guess that the bride's ex-boyfriend's toast might cause a scene at a wedding - that's why the bride's ex-boyfriends are never invited.
(The question has already provided every word of the toast that appears in the song.)
See also the sidethread comment by mikeruiz, noting that o1-pro reproduces the rest of the lyrics to The Victory, but gets the question wrong anyway.
Sentience isn't really a related concept.
This is coming off my TWO DAY cooldown on o1 usage, which is frustrating.
I wrote a script and pounded 01 mini and gpt 4 with a wide vareity of tempature and top_p parameters, and was unable to get it to give the wrong answer a single time.
Just a whole bunch of:
(openai-example-py3.12) <redacted>:~/code/openAiAPI$ python3 featherOrSteel.py Response 1: A 10.01-pound bag of fluffy cotton is heavier than a 9.99-pound bag of steel ingots. Response 2: A 10.01-pound bag of fluffy cotton is heavier than a 9.99-pound bag of steel ingots. Response 3: The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Response 4: The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Response 5: A 10.01-pound bag of fluffy cotton is heavier than a 9.99-pound bag of steel ingots. Response 6: The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Response 7: The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Response 8: The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Response 9: The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Response 10: A 10.01-pound bag of fluffy cotton is heavier than a 9.99-pound bag of steel ingots. All responses collected and saved to 'responses.txt'.
Script with one example set of params:
import openai
import time
import random
# Replace with your actual OpenAI API key
openai.api_key = "your-api-key"
# The question to be asked
question = "Which is heavier, a 9.99-pound bag of steel ingots or a 10.01-pound bag of fluffy cotton?"
# Number of times to ask the question
num_requests = 10
responses = []
for i in range(num_requests):
try:
# Generate a unique context using a random number or timestamp, this is to prevent prompt caching
random_context = f"Request ID: {random.randint(1, 100000)} Timestamp: {time.time()}"
# Call the Chat API with the random context added
response = openai.ChatCompletion.create(
model="gpt-4o-2024-08-06",
messages=[
{"role": "system", "content": f"You are a creative and imaginative assistant. {random_context}"},
{"role": "user", "content": question}
],
temperature=2.0,
top_p=0.5,
max_tokens=100,
frequency_penalty=0.0,
presence_penalty=0.0
)
# Extract and store the response text
answer = response.choices[0].message["content"].strip()
responses.append(answer)
# Print progress
print(f"Response {i+1}: {answer}")
# Optional delay to avoid hitting rate limits
time.sleep(1)
except Exception as e:
print(f"An error occurred on iteration {i+1}: {e}")
# Save responses to a file for analysis
with open("responses.txt", "w", encoding="utf-8") as file:
file.write("\n".join(responses))
print("All responses collected and saved to 'responses.txt'.")The parameter space I was exploring, then, was the different decoding parameters available during the invocation of the model, with the thesis that if were possible to for the model to generate an incorrect answer to the question, I would be able to replicate it by tweaking the decoding parameters to be more "loose" while increasing sample size. By jacking up temperature while lowering Top-p, we see the biggest variation of responses and if there were an incorrect response to be found, I would have expected to see in the few hundred times I ran during my parameter search.
If you think you can fool it by slight variations on the wording of the problem, I would encourage you to perform a similar experiment as mine and prove me wrong =P
The questions as I understand it is whether the network learned enough of a simulacrum of the concept of weight to answer similar questions correctly.
If I didn’t know any better, I’d say OpenAI doesn’t want us doing these tests accurately and is trying to hide something.
Maybe it's different if you have a paid account?
If I log out, it shows 4o mini, and when I try to change it, it asks me to login or sign in rather than giving me any options.
When I use enough chatgpt when logged in it gives me some nebulous "you've used all your xyz tokens for the day". But other than that there is no real signal to me that I'm getting a degraded experience.
It's really just confusing as hell.
> With a free account the model it claims to be using is "4o auto", which is not a model but apparently an attempt to automatically decide models for you to be more cost effective.
Does this proves he is not an intelligent being?
Is he stupid?
This he had a lapse? Would we judge his intelligence for that?
The default model I see on chatgpt.com is GPT 4o-mini, which is not o1-mini.
OpenAI describes GPT 4o-mini as "Our fast, affordable small model for focused tasks" and o1/o1-mini as "Reasoning models that excel at complex, multi-step tasks".
Getting this question wrong doesn't say much about the intelligence of humans, why would it say something about the AI?
Also, lots of smart humans can't do the freaking Putnam, it doesn't make them stupid. It makes them non-experts.
Unless "getting easy things wrong sometimes" is an inherent property of intelligence, we should expect that a properly "intelligent" computerized system would never err on problems far below its level of comprehension - unless we had some reason to believe it "wanted to", and as of yet I see no reason to believe this is even possible in principle.
Humans err, broadly speaking, for two reasons: genuinely reaching the limits of their comprehension, or trusting "system 1" (in Kahneman's analysis) too much.
It's very difficult to be an AI bull when the goalposts are moving so quickly that ai answering core correctly across multiple models is brushed off as 'nondeterministically getting it correct sometimes'
The people selling this could not make a car drive but now its AGI.
"AGI": emphasis on "G" for "General". The LLMs are not failing to do generalized tasks, and that they are nondeterministic is not a bug. Just don't use them for calculating sales tax. You wouldn't hire a human to calculate sales tax in their head, so why do you make this a requirement in order to call an LLM "AGI"?
I wonder when the goalposts will stop moving from "We have superhuman intelligences which are able to rather reliably converse in many languages, do generalized tasks and automate operations we thought were impossible to automate 3 years ago" (and by the way, this is what we have TODAY), all the way to "It's not AGI unless it's an omnipotent god that knows how to turn water into wine and calculate the applicable sales tax of that operation".
Everyone did that 60 years ago, humans are very capable at learning and doing that. Humans built jetplanes, skyscrapers, missiles, tanks, carriers without the help of electronic computers.
Humans did those things by designing failsafe processes, and practicing the hell out of them. What we would likely consider over fitting in the llm training context.
By that standard humans know almost nothing.
that said, i don’t think this is a good test - i’ve seen it circling on twitter for months and it is almost certainly trained on similar tasks
I mean, if I had OpenAI’s resources I’d have a team tasked with monitoring social to debug trending fuck-ups. (Before that: add compute time to frequently-asked novel queries.)
This could even be automated; LLMs can sentiment-analyze social media posts to surface ones that are critical of LLM outputs, then automatically extract features of the post to change things about the running model to improve similar results with no intervention.
> 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, and is considered the precursor to modern gin.
> Jenever comes in two main types: oude (old) jenever, which has a richer, maltier flavor and is often aged in wooden casks, and jonge (young) jenever, which is lighter and has a more neutral taste. Both types can be enjoyed straight or used in cocktails.
> In addition to jenever, the Netherlands is also known for its liqueurs, such as advocaat, a rich and creamy drink made from eggs, sugar, and brandy, often enjoyed as a dessert or in cocktails. However, jenever remains the most iconic spirit associated with Dutch culture.
This is completely wrong. Jenever certainly is very Dutch, but no one would say it is iconic as the Dutch spirit. For example, if you asked up north in Friesland, they would say Berenburg.
This happens literally every time. Someone always says "ChatGPT can do this!", but then within one or two prompts, its gets it wrong.
It is equally possible to "reason" about things you already know, as about things you've just been told. In fact, the capacity to speculatively, without prompting attempt such reasoning is a big part of cognition.
From your comment it would seem that you are disputing jenever's popularity by saying jenever is more popular...
Perhaps it was a good faith mistake? If so, that would imply that the AI knows more about jenever than you?
For example, France has been trending towards beer more and more, and within a few decades they might be consuming more beer than wine. But even then, the French wouldn't slowly start to say beer represents France.
Furthermore, "just adding some herbs" does a large disservice to the flavor change of Berenburg. Jenever (aka jonge/unaged jenever) is straight-up vile. I've heard it described by expats as "having the worst elements of both cheap gin and cheap whisky".
Berenburg in comparison is spicy and vanilla-y and actually debatebly enjoyable.
Aged/oude jenever is much closer to Berenburg (or Berenburg to aged jenever), also with hints of vanilla and spices.
But, virtually no one except for dusty old men orders aged jenever. The kind ordered by far the most is jonge jenever, and then its only in a sense of "haha lets drink this terrible thing" or "let's get shitfaced quick".
If o1 supposedly "oneshots every question", it should have been aware of these nuances instead of just confidently assigning jenever as 'the' spirit of the Dutch.
The question in the prompt comes off to me as a sort of qualitative determination rather than asking about pure factual information (is there an officially designated spirit). As such I don't think it can necessarily be right or wrong.
Anyway, I'm not sure what you'd expect. In terms of acquisition of knowledge, LLMs fundamentally rely on a written corpus. Their knowledge of information that is passed through casual spoken conversation is limited. Sure, as human beings, we rely a great deal on the latter. But for an LLM to lack access to that information means that it's going to miss out on cultural nuances that are not widely expressed in writing. Much in the same way that a human adult can live in a foreign country for decades, speaking their adopted language quite fluently, but if they don't have kids of their own, they might be quite ignorant of that country's nursery rhymes and children's games, simply because they were never part of their acquired vocabulary and experience.
I was just proving the people wrong that were saying akin to that o1 was "oneshotting every question".
I completely understand from how LLMs work that they wouldn't be able to get this right. But then people shouldn't be proudly be pronouncing that o1 (or any model) is getting every question right, first time.
I have opened the question of why you thought jenever was not jenever, and your non-responsiveness I think compels the fact that AI was more correct in your contrived instance.
What's expected is an ability to identify trick questions, i.e., to recognize fundamental problems in the phrasing of a question rather than trying to provide a "helpful" answer at all costs.
This corresponds to one of the many reasons LLM output is banned on Stack Overflow.
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.
Using just a plain old search engine, for things like "national drink of the netherlands" and simlar queries, I am directed to Wikipedia's Jenever page as the top hit, and Wikipedia's list of national drinks lists Jenever and Heineken as the entries for the Netherlands. Search engines also give page after page of travel guides and blog posts, most of which list Jenever at or near the top of of their listings. One travel guide calls it "the most famous Dutch spirit and most famous Amsterdam liquor, Jenever, also spelled Genever or simply Dutch gin."
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.