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How does GPT obtain its ability? Tracing emergent abilities of language models

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Re: How does GPT obtain its ability? Tracing emergent abilities of language models

#71

Post by 1123581321 »

bryan0 wrote: post #57
1123581321 wrote: post #12

Earlier quoted context omitted.

I've found it gets the math right most of the time in stories. For example: > Write a story about a veterinarian counting the number of total legs on two cats and a horse. He reports the total number of legs to his assistant so he can write them down. As the veterinarian entered the examination room, he was greeted by two playful cats and a majestic horse. He smiled and began his examination, carefully checking the h…

this is what I got with the same prompt: The veterinarian, Dr. Smith, walked into the examination room where two cats and a horse were waiting. He greeted the animals and their owners before approaching the first cat. The feline, a fluffy white and orange tabby, was lying on the examination table. Dr. Smith gently lifted one of its hind legs and counted the toes. "One, two, three, four," he muttered to himself before…

Ha. At least its opinion of horses is consistent.

Re: How does GPT obtain its ability? Tracing emergent abilities of language models

#72

Post by mettamage »

skybrian wrote: post #47
page_severian wrote: post #23

Earlier quoted context omitted.

These errors are common and important to note. Though what I find fascinating is how often I can prompt engineer around these errors by telling GPT to just be smarter and think more carefully. Just tried the following for instance. Q: You are a master logician, who can solve all manner of language problems and have excellent math skills. I have 2 cats and 1 horse. How many legs do they have in total? Show your reason…

Yes, this certainly helps. I find it ironic that you can get somewhat better results for nonfiction by giving it more clues about what it's supposed to be pretending to be. It's always pretending, though.

There’s an actual psychological effect for this as well [1]. Authors + uni are in the link. I forgot the name of the effect, don’t have time to do proper research.

[1] https://www.themarysue.com/lab-coats-increase-attention/#:~:....

Re: How does GPT obtain its ability? Tracing emergent abilities of language models

#73

Post by Gigachad »

vikp wrote: post #31
srajabi wrote: post #2

Amazing insight, particularly section 6. "- The two important but different abilities of GPT-3.5 are *knowledge* and *reasoning*. Generally, it would be ideal if we could *offload the knowledge part to the outside retrieval system and let the language model only focus on reasoning.* This is because: - The model’s internal knowledge is always cut off at a certain time. The model always needs up-to-date knowledge to an…

It's unclear to me how you could separate knowledge and reasoning: - Reasoning typically requires base knowledge to work from. A side effect of training reasoning is embedding knowledge into the model parameters. - Even if you offload the search portion (either through outputting special tokens that are postprocessed, or applying the model in multiple steps with postprocessing), you still need embedded knowledge for…

Doesn't seem to unreasonable to me. If I asked you "How old is Obama" and you had a data source which had the ages of every person, you don't need to know the answer from memory. Your reasoning tells you that to find out how old someone is you need to check the external resource and what to do with the info once you get it.

Re: How does GPT obtain its ability? Tracing emergent abilities of language models

#74

Post by sdenton4 »

skybrian wrote: post #47
page_severian wrote: post #23

Earlier quoted context omitted.

These errors are common and important to note. Though what I find fascinating is how often I can prompt engineer around these errors by telling GPT to just be smarter and think more carefully. Just tried the following for instance. Q: You are a master logician, who can solve all manner of language problems and have excellent math skills. I have 2 cats and 1 horse. How many legs do they have in total? Show your reason…

Yes, this certainly helps. I find it ironic that you can get somewhat better results for nonfiction by giving it more clues about what it's supposed to be pretending to be. It's always pretending, though.

Who isn't?

Re: How does GPT obtain its ability? Tracing emergent abilities of language models

#76

Post by [deleted] »

bryan0 wrote: post #57
1123581321 wrote: post #12

Earlier quoted context omitted.

I've found it gets the math right most of the time in stories. For example: > Write a story about a veterinarian counting the number of total legs on two cats and a horse. He reports the total number of legs to his assistant so he can write them down. As the veterinarian entered the examination room, he was greeted by two playful cats and a majestic horse. He smiled and began his examination, carefully checking the h…

this is what I got with the same prompt: The veterinarian, Dr. Smith, walked into the examination room where two cats and a horse were waiting. He greeted the animals and their owners before approaching the first cat. The feline, a fluffy white and orange tabby, was lying on the examination table. Dr. Smith gently lifted one of its hind legs and counted the toes. "One, two, three, four," he muttered to himself before…

[deleted]

Re: How does GPT obtain its ability? Tracing emergent abilities of language models

#77

Post by mannykannot »

1123581321 wrote: post #12
resource0x wrote: post #6

Earlier quoted context omitted.

Current GPT version is not very good at math, and it's not at all clear whether this can be fixed within a language model. Q: I have 2 cats and 1 horse. How many legs do they have in total? A: If you have two cats and one horse, then the total number of legs they have is 8. Cats typically have four legs, while horses have four legs as well. So two cats would have a total of 8 legs, and when you add the four legs of t…

I've found it gets the math right most of the time in stories. For example: > Write a story about a veterinarian counting the number of total legs on two cats and a horse. He reports the total number of legs to his assistant so he can write them down. As the veterinarian entered the examination room, he was greeted by two playful cats and a majestic horse. He smiled and began his examination, carefully checking the h…

Interesting - this is how we teach arithmetic to children. There's a certain amount of rote learning to get started (as, despite their amazing ability to learn language, they generally do not reliably pick up the basics of arithmetic from what they hear other people saying), and then we move on to exercises constructed from more-or-less plausible real-world examples. We certainly do not start with the axioms of number theory.

Re: How does GPT obtain its ability? Tracing emergent abilities of language models

#78

Post by ricopags »

mensetmanusman wrote: post #40

Is there anything like ChatGPT happening from China? They have almost 10x’d AI investment compared to the west, and I would be somewhat surprised if that investment combined with ubiquitous math skills failed to produce.

Sure!

WeLM[0] by WeChat has a demo[1] online. GLM-130B[2] is probably the most impressive. Huggingface has a demo[3] for them but it's kind of broken unless you tune it.

[0]https://arxiv.org/abs/2209.10372 [1]https://welm.weixin.qq.com/docs/playground/ [2]http://keg.cs.tsinghua.edu.cn/glm-130b/posts/glm-130b/ [3]https://huggingface.co/spaces/THUDM/GLM-130B

Re: How does GPT obtain its ability? Tracing emergent abilities of language models

#79

Post by PartiallyTyped »

Gigachad wrote: post #73
vikp wrote: post #31

Earlier quoted context omitted.

It's unclear to me how you could separate knowledge and reasoning: - Reasoning typically requires base knowledge to work from. A side effect of training reasoning is embedding knowledge into the model parameters. - Even if you offload the search portion (either through outputting special tokens that are postprocessed, or applying the model in multiple steps with postprocessing), you still need embedded knowledge for…

Doesn't seem to unreasonable to me. If I asked you "How old is Obama" and you had a data source which had the ages of every person, you don't need to know the answer from memory. Your reasoning tells you that to find out how old someone is you need to check the external resource and what to do with the info once you get it.

The required knowledge is around entity recognition, with "Obama" referring to the 44th POTUS, and not somebody else who happens to have the same surname (and there are multiple of them actually, at least 4 given his family).

Re: How does GPT obtain its ability? Tracing emergent abilities of language models

#80

Post by keepquestioning »

tunnuz wrote: post #51

> The ability of complex reasoning with chain-of-thought is likely to be a magical side product of training on code Seems reasonable, and magical indeed. Can any expert on the topic comment on this hypothesis?

This would win the Nobel Prize if true
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