4 ms·

For what it's worth, I think it feels like you're yelling into a hurricane because you are kind of clearly incorrect. Can you think of a few questions (using u

For what it's worth, I think it feels like you're yelling into a hurricane because you are kind of clearly incorrect. Can you think of a few questions (using unique nouns etc to avoid parroting) for which the answers require a world knowledge model, so that we can ask them to GPT-4? > LLMs are exceptionally good at transforming many aspects of language - its proficiency in coding is derived from this If you paste a Python script and ask GPT-4 what the script will output when run, it is usually correct. How does it achieve this without reasoning about the state of variables over time? I don't know why the "it's a language model" belief is so strong. For one thing, it's untrue technically: GPT-4 has been trained on image input as well (improving its world modeling as a result, even though it can only output text), so it is a multi-modal model, not a language model. For a second point, the fact that the model inputs images+text, and outputs text, doesn't prescribe what happens inside the model. That's just what's happening at the ends of the model. The best way to "predict the next token" from text input inside the model is to construct a world model which examines the causes of that text, and extracts consequences from that model. So that's what it does, with fairly poor reliability but clear capability.

goncalo-r 3y ago
It's not good at reasoning. I asked the following (the term substitutions are there to avoid parroting): Let's define the following terms: col means "and", pel means "or" and erf is equivalent to "col" if the first argument evaluates to "true", otherwise it's equivalent to "pel" can you evaluate the following statement? col(true, pel(false, true), erf(col(true, false), true)) And it said this: ... Next, we look at the expression "erf(col(true, false), true)". Since the first argument is true, erf is equivalent to col, which means we evaluate the entire expression as: col(true, false) = false Therefore, erf(col(true, false), true) = col(true, true) = true which is obviously wrong because the first arg is false. The final answer happens to be the same. So I then asked what about col(true, pel(false, true), erf(col(true, false), false)) and this is its full answer: Sure, let's evaluate the expression "col(true, pel(false, true), erf(col(true, false), false))" step by step: First, we look at the expression "pel(false, true)". This is an "or" operation, so we evaluate it as follows: pel(false, true) = true Next, we look at the expression "erf(col(true, false), false)". Since the first argument is true, erf is equivalent to col, which means we evaluate the entire expression as: col(true, false) = false Therefore, erf(col(true, false), false) = pel(false, false) = false Finally, we look at the expression "col(true, pel(false, true), erf(col(true, false), false))". Using the results from steps 1 and 2, we can evaluate this as: col(true, true, false) = true Therefore, the overall value of the statement "col(true, pel(false, true), erf(col(true, false), false))" is true. You can see it's mostly random nonsense masked as "reasoning". It's not even consistent across sentences. I assume if you do a bigger model, it might do better, but as soon as you increase the example size I suspect it will get it wrong again.
- jiggawatts 3y ago
  To be fair, I'm a fairly intelligent human (or so I'd like to think!), and I'm having a lot of trouble following this. I'd have to sit down with pencil and paper and do the transformations on it. Keep in mind that LLMs like GPT 4 do not have a scratchpad and can't back-track. Asking it a question is the rough equivalent of asking a human standing in the middle of the room a question. No paper, no whiteboard, nothing. Just a demand for a verbal answer then and there, from a standing start. I've only met a couple of humans who would be able to solve this problem like this one under those circumstances. Despite its impediments GPT 4 solved this correctly: First, let's break down the statement and evaluate the individual components. col(true, false) = true AND false = false pel(false, true) = false OR true = true erf(col(true, false), true) = erf(false, true) Since the first argument of erf evaluates to false, erf is equivalent to "pel": erf(false, true) = pel(false, true) = false OR true = true Now we can substitute the results back into the original statement: col(true, pel(false, true), erf(col(true, false), true)) = col(true, true, true) The col function acts as an AND operator, so: col(true, true, true) = true AND true AND true = true Therefore, the given statement evaluates to true. Alternatively, to give an idea of the upcoming "Wolfram Alpha" plugin, you can instead ask it to convert the expression into the equivalent Mathematica program: col[x_, y_] := And[x, y] pel[x_, y_] := Or[x, y] erf[x_, y_] := If[x, col[True, y], pel[False, y]] col[True, pel[False, True], erf[col[True, False], True]] Very nice! Unfortunately, it didn't notice that "col" is used with three arguments, not just two. This is a mistake many (most?) humans would make also. No worries, you can ask it to correct that. It'll apologise and produce this: col[args__] := Apply[And, {args}] pel[args__] := Apply[Or, {args}] erf[x_, y_] := If[x, col[True, y], pel[False, y]] col[True, pel[False, True], erf[col[True, False], True]] Which evaluates to: True There you go. Now go find me a random human who can do any of this from a standing start, no preparation, no warning, no tools, nothing but their wits. As a bonus, here's a related joke: Why did the logical functions col, pel, and erf form a band? Because together, they could create the perfect harmony: they knew when to "col"aborate, when to give each other some space and "pel"ay, and their "erf"forts were always well-timed!
  - goncalo-r 3y ago

goncalo-r 3y ago

It's not good at reasoning. I asked the following (the term substitutions are there to avoid parroting): Let's define the following terms: col means "and", pel means "or" and erf is equivalent to "col" if the first argument evaluates to "true", otherwise it's equivalent to "pel" can you evaluate the following statement? col(true, pel(false, true), erf(col(true, false), true)) And it said this: ... Next, we look at the expression "erf(col(true, false), true)". Since the first argument is true, erf is equivalent to col, which means we evaluate the entire expression as: col(true, false) = false Therefore, erf(col(true, false), true) = col(true, true) = true which is obviously wrong because the first arg is false. The final answer happens to be the same. So I then asked what about col(true, pel(false, true), erf(col(true, false), false)) and this is its full answer: Sure, let's evaluate the expression "col(true, pel(false, true), erf(col(true, false), false))" step by step: First, we look at the expression "pel(false, true)". This is an "or" operation, so we evaluate it as follows: pel(false, true) = true Next, we look at the expression "erf(col(true, false), false)". Since the first argument is true, erf is equivalent to col, which means we evaluate the entire expression as: col(true, false) = false Therefore, erf(col(true, false), false) = pel(false, false) = false Finally, we look at the expression "col(true, pel(false, true), erf(col(true, false), false))". Using the results from steps 1 and 2, we can evaluate this as: col(true, true, false) = true Therefore, the overall value of the statement "col(true, pel(false, true), erf(col(true, false), false))" is true. You can see it's mostly random nonsense masked as "reasoning". It's not even consistent across sentences. I assume if you do a bigger model, it might do better, but as soon as you increase the example size I suspect it will get it wrong again.

jiggawatts 3y ago
To be fair, I'm a fairly intelligent human (or so I'd like to think!), and I'm having a lot of trouble following this. I'd have to sit down with pencil and paper and do the transformations on it. Keep in mind that LLMs like GPT 4 do not have a scratchpad and can't back-track. Asking it a question is the rough equivalent of asking a human standing in the middle of the room a question. No paper, no whiteboard, nothing. Just a demand for a verbal answer then and there, from a standing start. I've only met a couple of humans who would be able to solve this problem like this one under those circumstances. Despite its impediments GPT 4 solved this correctly: First, let's break down the statement and evaluate the individual components. col(true, false) = true AND false = false pel(false, true) = false OR true = true erf(col(true, false), true) = erf(false, true) Since the first argument of erf evaluates to false, erf is equivalent to "pel": erf(false, true) = pel(false, true) = false OR true = true Now we can substitute the results back into the original statement: col(true, pel(false, true), erf(col(true, false), true)) = col(true, true, true) The col function acts as an AND operator, so: col(true, true, true) = true AND true AND true = true Therefore, the given statement evaluates to true. Alternatively, to give an idea of the upcoming "Wolfram Alpha" plugin, you can instead ask it to convert the expression into the equivalent Mathematica program: col[x_, y_] := And[x, y] pel[x_, y_] := Or[x, y] erf[x_, y_] := If[x, col[True, y], pel[False, y]] col[True, pel[False, True], erf[col[True, False], True]] Very nice! Unfortunately, it didn't notice that "col" is used with three arguments, not just two. This is a mistake many (most?) humans would make also. No worries, you can ask it to correct that. It'll apologise and produce this: col[args__] := Apply[And, {args}] pel[args__] := Apply[Or, {args}] erf[x_, y_] := If[x, col[True, y], pel[False, y]] col[True, pel[False, True], erf[col[True, False], True]] Which evaluates to: True There you go. Now go find me a random human who can do any of this from a standing start, no preparation, no warning, no tools, nothing but their wits. As a bonus, here's a related joke: Why did the logical functions col, pel, and erf form a band? Because together, they could create the perfect harmony: they knew when to "col"aborate, when to give each other some space and "pel"ay, and their "erf"forts were always well-timed!
- goncalo-r 3y ago