5 ms·

Yes shifting the goal posts and finding edge cases not well suited to LLMs, and also ignoring the chain of thought prompting. It can solve math word puzzles th

Yes shifting the goal posts and finding edge cases not well suited to LLMs, and also ignoring the chain of thought prompting. It can solve math word puzzles that are not in its training set. Yes you can find these edge cases. We know about these edge cases and that's just missing the point. There are countless examples of emergent properties in these LLMs which by definition are solving tasks outside of its training set. Its reasoning has been demonstrated on examples outside of its training set.

PaulDavisThe1st 3y ago
First of all, multiplying two numbers together is not "shifting the goal posts", but an absolutely basic test of any system that is claimed to able to do mathematical reasoning. I know that LLM's are not well suited for this, and that's because they cannot do arithmetic (among other things). So I tried a word puzzle that would also require simple multiplication: ------------------------------ PROMPT: i am going to cycle 1600 miles, with 234 miles on gravel roads. on paved roads i will ride at 1929288282 millimeters per second but on gravel I will ride at 0.00000000202 parsecs per second. How long will the journey take? ------------------------------- Now, I have to commend GPT on its ability to understand how you solve a problem like this, though that's not really very surprising given the huge numbers of such problems that exist in written materials. It precisely broke the problem down in a way that I suppose you could call "reasoning", but I would call "copying the formula for solving puzzles like this". And how did it do with the actual math? ---------------- 0.00000000202 parsecs per second is equivalent to 7499.6103827 miles per hour (mph), which we can calculate by converting parsecs to miles (1 parsec = 3.26 light-years = 19,173,511,840,000 miles) and dividing by the number of seconds in an hour: 0.00000000202 pc/s × 19,173,511,840,000 mi/pc ÷ 3600 s/hr = 7499.6103827 mph ---------- Utterly and completely wrong. Same issue with the millimeters per second computation. It is completely obvious why LLMs cannot do this. They cannot perform even basic arithmetic reasoning, and even more fundamentally, the ONLY capability they have is to create likely responses to prompts. For some things, this is extraordinarily (and scarily) powerful. But it is not reasoning.
- Mike_12345 3y ago
  You are so narrowly fixated on this one specific domain which is an edge case with LLMs. ChatGPT was trained specifically to solve "natural language understanding tasks". You are missing the forest for the trees and ignoring everything else it's good at solving outside of its training set. The emergent properties of neural networks should not be so casually dismissed. You're arguing that since its not perfect at arithmetic reasoning then its not capable of any degree of reasoning in any domain. That is an oversimplification and just doesn't make logical sense.
  - PaulDavisThe1st 3y ago
    Actually, I'm not narrowly fixated at all. I do not believe that any part of any current or future LLM (i.e. using the same fundamental architecture) is capable of reasoning, or in fact, capable of anything other than, essentially, doing a really, really, really good job of generating the next word in a response. I am all about emergent properties of neural networks, but I absolutely do not believe that LLMs have them, specifically because of the way they are designed. However, as to the specifics, people who seem to believe otherwise claim that they can reason, and so that's merely one specific angle of attack: to show that they cannot reason, and that in fact, everything they do is implicitly contained in their training set. As I've already said, what they can do is enormously powerful and in many (most?) ways entirely unexpected, so I still regard the advent of LLMs as extremely significant, both from a practical but also a scientific point of view. I think it may force a revision in the most basic aspects of understanding human speech behavior, for example. Nevertheless, I do not believe that anybody is served by believing that these systems can do things that they cannot. I do not understand why the seemingly magic results of these systems is leading so many into a denial of what they actually do.
    - Mike_12345 3y ago
      > I am all about emergent properties of neural networks, but I absolutely do not believe that LLMs have them, specifically because of the way they are designed. Even when faced with evidence that contradicts your beliefs and proves that you are wrong? LLMs are a type of neural network. What fundamentally prevents LLMs from having emergent abilities while other neural networks do have them? How do you explain the emergent abilities that we have actually observed in LLMs?
nl 3y ago
I don't agree that LLMS can't reason, but literally saying "Make up your own math problems and go for it", him doing that and it failing really isn't moving the goal posts. LLMs are not good at math. But this is a subset of reasoning. Chain-of-thought on logical inference tasks (using fake labels so we are outside the training set) shows they can do reasonably well at these. Nevertheless, it's likely that the best approach for pure reasoning tasks will be to connect a LLM to a real inference engine (datalog or something) and rely on the LLM to perform the mapping to the inference engine inputs and outputs. This is similar to the "System 1" and "System 2" models of human thought.

PaulDavisThe1st 3y ago

First of all, multiplying two numbers together is not "shifting the goal posts", but an absolutely basic test of any system that is claimed to able to do mathematical reasoning. I know that LLM's are not well suited for this, and that's because they cannot do arithmetic (among other things). So I tried a word puzzle that would also require simple multiplication: ------------------------------ PROMPT: i am going to cycle 1600 miles, with 234 miles on gravel roads. on paved roads i will ride at 1929288282 millimeters per second but on gravel I will ride at 0.00000000202 parsecs per second. How long will the journey take? ------------------------------- Now, I have to commend GPT on its ability to understand how you solve a problem like this, though that's not really very surprising given the huge numbers of such problems that exist in written materials. It precisely broke the problem down in a way that I suppose you could call "reasoning", but I would call "copying the formula for solving puzzles like this". And how did it do with the actual math? ---------------- 0.00000000202 parsecs per second is equivalent to 7499.6103827 miles per hour (mph), which we can calculate by converting parsecs to miles (1 parsec = 3.26 light-years = 19,173,511,840,000 miles) and dividing by the number of seconds in an hour: 0.00000000202 pc/s × 19,173,511,840,000 mi/pc ÷ 3600 s/hr = 7499.6103827 mph ---------- Utterly and completely wrong. Same issue with the millimeters per second computation. It is completely obvious why LLMs cannot do this. They cannot perform even basic arithmetic reasoning, and even more fundamentally, the ONLY capability they have is to create likely responses to prompts. For some things, this is extraordinarily (and scarily) powerful. But it is not reasoning.

Mike_12345 3y ago
You are so narrowly fixated on this one specific domain which is an edge case with LLMs. ChatGPT was trained specifically to solve "natural language understanding tasks". You are missing the forest for the trees and ignoring everything else it's good at solving outside of its training set. The emergent properties of neural networks should not be so casually dismissed. You're arguing that since its not perfect at arithmetic reasoning then its not capable of any degree of reasoning in any domain. That is an oversimplification and just doesn't make logical sense.
- PaulDavisThe1st 3y ago
  Actually, I'm not narrowly fixated at all. I do not believe that any part of any current or future LLM (i.e. using the same fundamental architecture) is capable of reasoning, or in fact, capable of anything other than, essentially, doing a really, really, really good job of generating the next word in a response. I am all about emergent properties of neural networks, but I absolutely do not believe that LLMs have them, specifically because of the way they are designed. However, as to the specifics, people who seem to believe otherwise claim that they can reason, and so that's merely one specific angle of attack: to show that they cannot reason, and that in fact, everything they do is implicitly contained in their training set. As I've already said, what they can do is enormously powerful and in many (most?) ways entirely unexpected, so I still regard the advent of LLMs as extremely significant, both from a practical but also a scientific point of view. I think it may force a revision in the most basic aspects of understanding human speech behavior, for example. Nevertheless, I do not believe that anybody is served by believing that these systems can do things that they cannot. I do not understand why the seemingly magic results of these systems is leading so many into a denial of what they actually do.
  - Mike_12345 3y ago
    > I am all about emergent properties of neural networks, but I absolutely do not believe that LLMs have them, specifically because of the way they are designed. Even when faced with evidence that contradicts your beliefs and proves that you are wrong? LLMs are a type of neural network. What fundamentally prevents LLMs from having emergent abilities while other neural networks do have them? How do you explain the emergent abilities that we have actually observed in LLMs?

nl 3y ago

I don't agree that LLMS can't reason, but literally saying "Make up your own math problems and go for it", him doing that and it failing really isn't moving the goal posts. LLMs are not good at math. But this is a subset of reasoning. Chain-of-thought on logical inference tasks (using fake labels so we are outside the training set) shows they can do reasonably well at these. Nevertheless, it's likely that the best approach for pure reasoning tasks will be to connect a LLM to a real inference engine (datalog or something) and rely on the LLM to perform the mapping to the inference engine inputs and outputs. This is similar to the "System 1" and "System 2" models of human thought.