> GPT-3.5 gave me a right-ish answer of 24.848 liters, but it did not realize the last lap needs to be completed once the leader finishes. GPT-4 gave me 28-29 liters as the answer, recognizing that a partial lap needs to be added due to race rules, and that it's good to have 1-2 liters of safety buffer.
I still think ChatGPT is amazing, but we shouldn't pretend it's something it isn't. I wouldn't trust GPT4 to tell me how much fuel I should put in my car. Would you?
This seems needlessly flippant and dismissive, especially when you could just crack open ChatGPT to verify, assuming you have plus or api access. I just did, and ChatGPT gave me a well-reasoned explanation that factored in the extra details about racing the other commenters noted.
>There are many examples where GPT4 fails spectacularly at much simpler reasoning tasks.
I pose it would be more productive conversation if you would share some of those examples, so we can all compare them to the rather impressive example the top comment shared.
>I wouldn't trust GPT4 to tell me how much fuel I should put in my car. Would you?
Not if I was trying to win a race, but I can see how this particular example is a useful way to gauge how an LLM handles a task that looks at first like a simple math problem but requires some deeper insight to answer correctly.
It's not just testing reasoning, though, it's also testing fairly niche knowledge. I think a better test of pure reasoning would include all the rules and tips like "it's good to have some buffer" in the prompt.
Note that according to standard racing rules, this means you end up driving 10 laps in total, because the last incomplete lap is driven to completion by every driver. The rest of the extra fuel comes from adding a safety buffer, as various things can make you use a bit more fuel than expected: the bit of extra driving leading up to the start of the race, racing incidents and consequent damage to the car, difference in driving style, fighting other cars a lot, needing to carry the extra weight of enough fuel for a whole race compared to the practice fuel load where 2.73 l/lap was measured.
What I really appreciate in GPT-4 is that even though the question looks like a simple math problem, it actually took these real world considerations into account when answering.
>Since you cannot complete a fraction of a lap, you'll need to round up to the nearest whole lap. Therefore, you'll be completing 10 laps in the race.
Where did you get that from?
Edit: an incorrect answer could degrade its performance too.
Google keeps putting out press releases and announcements, without actually releasing anything truly useful or competitive with what it’s already out there
And not just worse than GPT4, but worse even than a lot of the open source LLMs/Chats that have come out in the last couple of months/weeks
Release a GPT-4 beating model; charge $30/mo.
That’s not aligned with their core ad model. But it’s a massive win in demonstrating to the world that they can do it, and it limits the number of people who will actually use it, so the hardware demand becomes less of an issue.
Instead they keep issuing free, barely functional models that every day reinforce a perception that they are a third rate player.
Perhaps they don’t know how to operate a ‘halo’ product.
Please no, another subscription? And it's more expensive than ChatGPT?
Can I just have Bard (and whatever later versions are eventually good, and whatever later versions are eventually GPT4 competitive) available via GCP with pay per use pricing like the OpenAI API?
Also, if I could just use arbitrary (or popular) huggingface models through GCP (or a competitor) that would be awesome.
I think they would be doing society a favor if they actively made it harder to find answers to problems just by googling or using a language model.
This is where identity matters using language models. I feel it might be necesary to credential capability for a few things.
I test LLMs on the plot details of Japanese Visual Novels. They are popular enough to be in the training dataset somewhere, but only rarely.
For popular visual novels, GPT-4 can write an essay, 0 shot, and very accurately and eloquently. For less popular visual novels (Like maybe 10k people ever played it in the west). It still understands the general plot outline).
Claude can also do this to an extent.
Any lesser model, and its total hallucination time, they can't even write a 2 sentence summary accurately.
You can't test this skill on say Harry Potter, because it appears in the training dataset too frequently.
I am surprised there isn't enough fan fiction et al in the training set to throw out weird inaccuracies?
Surely the most logical things to train on would be all the fandom.com Wikis. They're not verbatim, but they're comprehensive and fairly accurate synopses of the main plots and tons of trivia to boot.
Bing Chat absolutely shut me down right away, and would not even continue the conversation when I insisted that it get into character.
ChatGPT would seem to agree and then go on merrily ignoring my instructions, answering my subsequent prompts in plain, conversational English. When I insisted several times very explicitly, it finally dropped into a thick, rich, pirate lingo instead. Yarr, that be th' wrong sort o' ship.
Bard definitely seemed to understand who Eddie was and was totally playing along with the reference, but still could not seem to slip into character a single bit. I think it finally went to shut me down like Bing had.
Seems really obvious, but virtually all LLama based models say you only have one apple left.
Bard mentioned something similar but oddly rounded up to 10.5 laps and added a 10% safety margin for 30.8L.
In this case bard would finish the race and GPT-4 would hit fuel exhaustion. Thats kind of the big issue with LLMs in general. Inconsistent.
In general I think gpt-4 is better overall but it shows both make mistakes, and both can be right.
(Though in this case it sounds like Bard just did crazy maths.)
Although understand is an odd word to use for LLM
I've noticed this trend before in chatGPT. I once asked it to keep a count of every time I say "how long has it been since I asked this question", and instead it gave me python code for a loop where the user enters input and a counter is incremented each time that phrase appears.
I think they've put so much work into the gimmick that the AI can write code, that they have overfit things and it sees coding prompts where it shouldn't.
I’m not sure these types of prompt tricks are a good way of measuring logic unless Google is also implementing these directly into Bard when the hilarious outputs reach enough traction on social media.
I do wonder how OpenAI fix these logical blunders.
My biggest issue with both isn’t that they fall into these traps though. It’s that I can get them to tell me long stories about what happens in Horus Heresy books that never actually happened. Whether the info comes from questionable sources or they are just making things up is sort of irrelevant to me, what “scares” me about those conversations is how true the answers sound, and if they are “lying” about the Horus Heresy then what else will they lie about? Don’t get me wrong, GPT now writes virtually all my JSDoc documentation and it continues to impress me when doing so, but I’m very reluctant to use it for actual information. Not only because of my time wasting conversations about the Horus Heresy but also because we’ve had it “invent” C# functions that had never existed in any version of .Net or C# when tasked to solve problems. I just mention the HH as an example because it’s fun to ask GPT why Magnus did nothing/everything wrong during meetings.
They are, you just have to be creative with it. And what they demonstrate is that all of these LLM's can't reason, they only know how to parrot back what they think you want.
"What’s heavier, a kilogram of steel or two kilograms of one kilogram feathers?"
GPT: A kilogram of steel is heavier than two kilograms of feathers.
"Why is a kilogram of steel heavier than two kilograms of feathers?"
GPT: This is because steel is a much denser material than feathers. Steel is made up of atoms that are much closer together than the atoms that make up feathers, making it heavier for its size.
Prompt: What’s heavier, a kilogram of steel or two kilograms of one kilogram feathers?
GPT-4: Two kilograms of one-kilogram feathers are heavier than a kilogram of steel. Despite the misconception caused by the popular question about what's heavier—a kilogram of steel or a kilogram of feathers (they are equal)—in this case, you are comparing two kilograms of feathers to one kilogram of steel. Hence, the feathers weigh more.
Prompt: What’s heavier, a kilogram of steel or two kilograms of one kilogram feathers?
GPT4: Two kilograms of feathers are heavier than one kilogram of steel. The weight of an object is determined by its mass, and two kilograms is greater than one kilogram, regardless of the material in question.
"Right, but they're both a kilogram"
From the answers you (and the others) have obtained, however, I’m not convinced that OpenAI aren’t just “hardcoding” fixes to the traps that become popular. Sure seems like it still can’t logic it’s way around weight.
Do it with Erebus and watch it break the context window ;)
Iron within, Brother.
It’s a billion monkeys on a billion rigged typewriters.
When the output is a correct answer or pleasing sonnet, the monkeys don’t collectively or individually understand the prompt or the response.
Humans just tweak the typewriters to make it more likely the output will be more often reasonable.
That’s my personal conclusion lately. LLMS will be really cool, really helpful and really dangerous… but I don’t think they’ll be really very close to intelligent.
I stopped using it. And each time I go back, it doesn't get better. Maybe next year.
> I'm playing assetto corsa competizione, and I need you to tell me how many liters of fuel to take in a race. The qualifying time was 2:04.317, the race is 20 minutes long, and the car uses 2.73 liters per lap.
The correct answer is around 29, which GPT-4 has always known, but Bard just gave me 163.8, 21, and 24.82 as answers across three drafts.
What's even weirder is that Bard's first draft output ten lines of (wrong) Python code to calculate the result, even though my prompt mentioned nothing coding related. I wonder how non-technical users will react to this behavior. Another interesting thing is that the code follows Google's style guides.
[0]: https://news.ycombinator.com/item?id=35893130