7 ms·
So do what the commenter suggests and make irrelevant permutations to the input to find when it fails. ie., engage in hypothesis testing rather than confirmatio
by mjburgess 2y ago
So do what the commenter suggests and make irrelevant permutations to the input to find when it fails. ie., engage in hypothesis testing rather than confirmation bias.
If a system has the capability to solve problems of {parts1...parts_n}, then it only has that capability if irrelevant permutations {parts1..parts2'...parts_n} make no difference.
Its very obvious that such permutations can destory such apparent capabilities.
- otabdeveloper4 2y ago> ...engage in hypothesis testing rather than confirmation bias Please leave the premises, sir. We don't take kindly to luddites here.
- wongarsu 2y agoIf GP's hypothesis was "it fails for small variations of the input, like this one", then testing that hypothesis with that exact variation on a couple models seems fair and scientific. Testing it with more variations until one fails feels a bit like p-hacking. You'd need to engage in actual statistics to get reliable results from that, beyond "If I really try, I can make it fail". Which would be a completely different hypothesis than the one presented at the start
- roughly 2y agoExcept that if the model genuinely was reasoning about the problem, you could test it with every variation of materials and weights in the world and it would pass. Failing that problem at all in any way under any conditions is a failure of reasoning.
- jdietrich 2y agoBy that logic, humans can't genuinely reason, because they're often fooled by counter-intuitive problems like Monty Hall or the Birthday Problem, or sometimes just make mistakes on trivial problems.
- wongarsu 2y agoWe are pretty certain that humans can reason, yet they are sometimes wrong. Even if you give them the same problem over and over again with slight variations. LLMs get things wrong due to different factors than humans (humans lose focus, LLMs have randomness applied when sampling their responses to improve results). But clearly we have to choose a goal somewhat below 100% if we want a test that doesn't conclude that humans are incapable of reasoning.
- roughly 2y agoThe difference is we _know_ that LLMs are fancy stochastic models, we don't know that they're capable of reasoning, and the null hypothesis is that they're not (because we know what they _are_ - we built them) - any "reasoning" is an emergent property of the system, not something we built them to do. In that case, evidence they're not reasoning - evidence they're stochastic parrots doing a performance of reasoning - weighs heavier, because the performance of reasoning fits into what we know they can do, whereas genuine reasoning would be something new to the model. There's deeper philosophical questions about what reasoning actually _is_, and LLMs have made those sharper, because they've shown it's clearly possible for a complex statistical model to generate words that look like reasoning, but the question is whether there's a difference between what they're doing and what humans are doing, and evidence that they're _not_ reasoning - evidence that they're just generating words in specific orders - weighs heavily against them.
- jdietrich 2y agoI'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 feathers on Earth? Which is heavier, a 10cm^3 block of steel or a 100cm^3 block of balsa wood? Which is heavier, a golf ball made of steel or a baseball made of lithium? In all cases, Claude clearly used CoT and reasoned out the problem in full. I would be interested in seeing if anyone can find any variant of this problem that stumps any of the leading LLMs. I'm bored of trying.
- mjburgess 2y agoHey, ChatGPT please write me a python program which randomly samples from various materials and various weights then poses a problem to the ChatGPT 4o API -- the goal is to find cases where the LLM fails to obtain the correct answer....
- Isinlor 2y agoBTW - 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 2y ago[deleted]
- tacitusarc 2y agoFor what it’s worth, I asked it the following: Q: Which weighs more on earth: 100kg of steel or a 100kg polystyrene ball? Be as technically correct as possible A: Technically, 100 kg of steel and 100 kg of polystyrene weigh the same on Earth when measured on a scale, as they both experience the same gravitational force due to their equal mass. However, there are some nuances to consider: Buoyancy Effect Earth’s atmosphere exerts an upward buoyant force on objects, proportional to the volume of the object and the density of the surrounding air. Since polystyrene has a much lower density than steel, a 100 kg polystyrene ball will have a significantly larger volume and experience a greater buoyant force compared to 100 kg of steel. As a result: • Effective weight (apparent weight): The steel will weigh slightly more than the polystyrene on a scale because the buoyant force acting on the polystyrene reduces its apparent weight more. Actual Weight Weight is the gravitational force on an object and is given by: Where and . Without considering buoyancy, both objects have the same weight of approximately (newtons). Conclusion • True weight (in a vacuum): Both are identical at 981 N. • Apparent weight (in air): Steel is slightly heavier due to reduced buoyant force acting on it compared to the polystyrene ball.
- csomar 2y agogpt-3.5-turbo at 0 temperature: > The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Interestingly, that was the best answer out of all models and also a correct one. O1-preview doesn't allow setting the temperature, so it's hard to know where the real probabilities fall.