[HN Gopher] How does GPT obtain its ability? Tracing emergent ab...
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How does GPT obtain its ability? Tracing emergent abilities of
language models
Author : headalgorithm
Score : 63 points
Date : 2022-12-14 21:07 UTC (1 hours ago)
(HTM) web link (yaofu.notion.site)
(TXT) w3m dump (yaofu.notion.site)
| seydor wrote:
| Not sure about all its filters being RL. Sometimes it seems to
| flag its output as inappropriate because of a single word (or
| none at all). Also it has asymmetric behavior, e.g. it will make
| a joke about men but refuse to make one about women
| jimmaswell wrote:
| Imagine Wolfram's system with GPT integrated. I hope they're
| working on that.
| seydor wrote:
| they seem to be very different and not at all compatible
| approaches.
| resource0x wrote:
| 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 the horse, that
| brings the total number of legs to 8.
| sebzim4500 wrote:
| It would have got it right if you got it to show its working.
|
| That's the trick to doing computations with LLMs, the
| computation per token is constant so you want to split the
| computation over many tokens.
|
| > I have 2 cats and 1 horse. How many legs do they have in
| total? show your working.
|
| > If you have 2 cats and 1 horse, they would have a total of
| 10 legs. Cats typically have 4 legs, so 2 cats would have 2 x
| 4 = <<24=8>>8 legs. Horses have 4 legs, so 1 horse would have
| 1 x 4 = <<14=4>>4 legs. Together, the 2 cats and 1 horse
| would have 8 + 4 = <<8+4=12>>12 legs.
| amelius wrote:
| > That's the trick to doing computations with LLMs, the
| computation per token is constant so you want to split the
| computation over many tokens.
|
| Could you explain this a bit more? What are LLMs? Why is
| the number of tokens different in your approach?
| [deleted]
| smallerfish wrote:
| It's very reactive to the prompt:
|
| > i have 23 horses each with a normal number of legs, along
| with 14 dogs, each with a normal number of legs. how many
| legs in total do all of the animals have?
|
| > Based on the information you provided, it sounds like you
| have 23 horses and each horse has 4 legs, and you have 14
| dogs and each dog has 4 legs. If that is correct, then the
| total number of legs among all of the animals would be 23 x 4
| + 14 x 4 = 92 + 56 = <<234+144=148>>148 legs.
| moffkalast wrote:
| You know funny thing, for comparison I just entered that
| exact prompt into the 3 gig sized flan-t5 and I get the
| following:
|
| > The horses have 23 * 4 = 126 legs. The dogs have 14 * 4 =
| 84 legs. The total number of legs is 126 + 84 = 204. The
| answer: 204.
|
| Very very similar, but completely wrong results.
| 1123581321 wrote:
| 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 health of
| each animal.
|
| After completing his examinations, the veterinarian turned to
| his assistant and said, "I need you to write down the total
| number of legs for these three animals."
|
| The assistant nodded and picked up a pen and paper. "Okay,
| let's see," the veterinarian said as he counted the legs on
| the cats. "Each cat has four legs, so that's eight legs for
| the two cats."
|
| Next, he moved on to the horse. "And this beautiful horse has
| four legs as well, for a total of twelve legs."
|
| The assistant quickly wrote down the numbers as the
| veterinarian spoke. "Twelve legs total," the veterinarian
| confirmed. "Make sure to include that in their medical
| records."
|
| The assistant nodded and continued to take notes as the
| veterinarian continued to provide updates on the animals'
| health. As they finished up the examinations, the
| veterinarian couldn't help but smile, knowing that he was
| able to help these wonderful creatures.
| amelius wrote:
| Perhaps you can ask GPT about what is wrong in the answer and
| how it would fix that? This would give a general approach:
|
| Q: [Question]
|
| A: [Answer]
|
| Q: What is wrong in "[Answer]" and how would you fix that?
|
| A: [Improved answer]
| srajabi wrote:
| 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
| answer up-to-date questions. - Recall we have discussed that is
| 175B parameter is heavily used for storing knowledge. If we could
| offload knowledge to be outside the model, then the model
| parameter might be significantly reduced such that eventually, it
| can run on a cellphone (call this crazy here, but ChatGPT is
| already science fiction enough, who knows what the future will
| be)."
|
| & "Yet there was a WebGPT paper published in Dec 2021. It is
| likely that this is already tested internally within OpenAI."
|
| It definitely feels like this may be the next step in making this
| kind of system robust. It ends up being an interface for search.
| kelseyfrog wrote:
| See REALM[1] for some older(2 years) work on this idea.
|
| 1. https://arxiv.org/abs/2002.08909
| adamsmith143 wrote:
| The problem with ChatGPT's "knowledge" is that it isn't
| trustworthy. It will happily output very confident sounding
| nonsense, or blatantly incorrect statements. We need a way to
| verify how accurate it's outputs are
| belter wrote:
| ChatGPT made this nice COBOL program to create an S3 Bucket,a
| technical impossibility...
|
| IDENTIFICATION DIVISION. PROGRAM-ID. CREATE-S3-BUCKET.
|
| ENVIRONMENT DIVISION. CONFIGURATION SECTION.
|
| INPUT-OUTPUT SECTION.
|
| DATA DIVISION. FILE SECTION.
|
| WORKING-STORAGE SECTION. 01 AWS-ACCESS-KEY PIC X(20). 01 AWS-
| SECRET-KEY PIC X(40). 01 BUCKET-NAME PIC X(255).
|
| PROCEDURE DIVISION. CREATE-BUCKET. MOVE AWS-ACCESS-KEY TO
| AWS-ACCESS-KEY-VAR MOVE AWS-SECRET-KEY TO AWS-SECRET-KEY-VAR
| MOVE BUCKET-NAME TO BUCKET-NAME-VAR INVOKE AWS-S3 "CREATE-
| BUCKET" USING AWS-ACCESS-KEY-VAR AWS-SECRET-KEY-VAR BUCKET-
| NAME-VAR
| nathias wrote:
| so, much like other knowledge sources?
| jtxt wrote:
| One way I tried to do this is by having it write an answer,
| and a footnote reference at each fact. [1] then list search
| terms that be used to verify each claim, then I would respond
| with the url and quotes from found pages for each one, then
| have it rewrite the answer based on that information and cite
| the sources. I think something this direction can be
| automated. I saw someone do this with math and other tasks,
| that would talk to a connected program before answering.
| moffkalast wrote:
| ChatGPT to be employed in marketing positions immediately.
| rightbyte wrote:
| How much disk space does 175B parameters use? A float or half
| precision float per parameter or does it need pointers to
| connections too?
|
| Given how responses are generated in seconds and for free I am
| fairly sure it could run on a desktop computer.
| moyix wrote:
| One float per param, so naively 175*4 = ~700GB on disk. Most
| recent models are trained in FP16 or BF16 so 350GB. And
| there's some work on quantizing them to INT8 so knock that
| down to a mere 175GB. You can definitely run it on a desktop
| computer using RAM and NVME offload to make up for the fact
| that you probably don't have 175GB of GPU memory available,
| but it won't be fast: https://huggingface.co/blog/bloom-
| inference-pytorch-scripts
|
| OpenAI generates responses so fast by doing the generation in
| parallel across something like 8x80GB A100s (I don't know the
| exact details of their hardware setup, but NVIDIA's open
| FasterTransformer library achieves low latency for large
| models this way).
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