4 ms·

> Easy for who? Consider things from a different angle. The hype men promoting the latest LLMs say the newest models produce PhD-level performance across a br

> Easy for who? Consider things from a different angle. The hype men promoting the latest LLMs say the newest models produce PhD-level performance across a broad suite of benchmarks; some have even claimed that ChatGPT 4 is an early version of an AGI system that could become super-intelligent. So the advertising teams have set the bar very high indeed. As smart as the smartest humans around, maybe smarter. The bar they have set for themselves doesn't allow for any "oh but the tokenisation" excuses.

diggan 2y ago
> The hype men promoting the latest LLMs say the newest models produce PhD-level performance across a broad suite of benchmarks; some have even claimed that ChatGPT 4 is an early version of an AGI system that could become super-intelligent. Alright, why don't you go and discuss this with the people who say those things instead? No one made those points in this subthread, so not sure why they get brought up here.
danielmarkbruce 2y ago
Most human math phd's have all kinds of shortcomings. The idea that finding some "gotchas" shows that they are miles off the mark with the hype is absurd.
- michaelt 2y ago
  > Most human math phd's have all kinds of shortcomings. I know a great many people with PhDs. They're certainly not infallible by any means, but I can assure you, every single one of them can correctly count the number of occurrences of the letter 'r' in 'strawberry' if they put their mind to it.
  - danielmarkbruce 2y ago
    I'll bet said phds can't answer the equivalent question in a language they don't understand. LLMs don't speak character level english. LLMs are, in some stretched meaning of the word, illiterate. If LLMs used character level tokenization it would work just fine. But we don't do that and accept the trade off. It's only folks who have absolutely no idea how LLMs work that find the strawberry thing meaningful.
    - sdesol 2y ago
      > It's only folks who have absolutely no idea how LLMs work that find the strawberry thing meaningful. I think it is meaningful in that it highlights how we need to approach things a bit differently. For example, instead of asking "How many r's in strawberry?", we say "How many r's in strawberry? Show each character in an ordered list before counting. When counting, list the position in the ordered list." If we do this, every model that I asked got it right. https://beta.gitsense.com/?chat=167c0a09-3821-40c3-8b0b-8422223ff656 https://beta.gitsense.com/?chat=167c0a09-3821-40c3-8b0b-8422... There are quirks we need to better understand and I would say the strawberry is one of them. Edit: I should add that getting LLMs to count things might not be the best way to go about it. Having it generate code to count things would probably make more sense.
      - HarHarVeryFunny 2y ago
        I was impressed with Claude Sonnet the other day - gave it a photo of my credit card bill (3 photos actually - long bill) and asked it to break it down by recurring categories, counting anything non-recurring as "other". It realized without being asked that a program was needed, and wrote/ran it to give me what I asked for.
        sdesol 2y ago
        I think this will be the future. LLMs will know enough to know that it should hand things off to something else.
        danielmarkbruce 2y ago
        It's the present. ChatGPT, for example, is an application. It uses models, but it does all kinds of stuff at the application level too.
        sdesol 2y ago
        Is the instructions to pass something off built into the model or is it clever prompting or a bit of both?
  - CamperBob2 2y ago
    I know a great many people with PhDs. They're certainly not infallible by any means, but I can assure you, every single one of them can correctly count the number of occurrences of the letter 'r' in 'strawberry' if they put their mind to it. So can the current models. It's frustrating that so many people think this line of reasoning actually pays off in the long run, when talking about what AI models can and can't do. Got any other points that were right last month but wrong this month?
    - danielmarkbruce 2y ago
      There are always going to be doubters on this. It's like the self driving doubters. Until you get absolute perfection, they'll point out shortcomings. Never mind that humans have more holes than swiss cheese.
  - visarga 2y ago
    Humans tasked to count how many vowels are in "Pneumonoultramicroscopicsilicovolcanoconiosis" (a real word), without seeing the word visually, just from language, would struggle. Working memory limits. We're not that different, we fail too.

diggan 2y ago

> The hype men promoting the latest LLMs say the newest models produce PhD-level performance across a broad suite of benchmarks; some have even claimed that ChatGPT 4 is an early version of an AGI system that could become super-intelligent. Alright, why don't you go and discuss this with the people who say those things instead? No one made those points in this subthread, so not sure why they get brought up here.

danielmarkbruce 2y ago

Most human math phd's have all kinds of shortcomings. The idea that finding some "gotchas" shows that they are miles off the mark with the hype is absurd.

michaelt 2y ago
> Most human math phd's have all kinds of shortcomings. I know a great many people with PhDs. They're certainly not infallible by any means, but I can assure you, every single one of them can correctly count the number of occurrences of the letter 'r' in 'strawberry' if they put their mind to it.
- danielmarkbruce 2y ago
  I'll bet said phds can't answer the equivalent question in a language they don't understand. LLMs don't speak character level english. LLMs are, in some stretched meaning of the word, illiterate. If LLMs used character level tokenization it would work just fine. But we don't do that and accept the trade off. It's only folks who have absolutely no idea how LLMs work that find the strawberry thing meaningful.
  - sdesol 2y ago
    > It's only folks who have absolutely no idea how LLMs work that find the strawberry thing meaningful. I think it is meaningful in that it highlights how we need to approach things a bit differently. For example, instead of asking "How many r's in strawberry?", we say "How many r's in strawberry? Show each character in an ordered list before counting. When counting, list the position in the ordered list." If we do this, every model that I asked got it right. https://beta.gitsense.com/?chat=167c0a09-3821-40c3-8b0b-8422223ff656 https://beta.gitsense.com/?chat=167c0a09-3821-40c3-8b0b-8422... There are quirks we need to better understand and I would say the strawberry is one of them. Edit: I should add that getting LLMs to count things might not be the best way to go about it. Having it generate code to count things would probably make more sense.
    - HarHarVeryFunny 2y ago
      I was impressed with Claude Sonnet the other day - gave it a photo of my credit card bill (3 photos actually - long bill) and asked it to break it down by recurring categories, counting anything non-recurring as "other". It realized without being asked that a program was needed, and wrote/ran it to give me what I asked for.
      - sdesol 2y ago
        I think this will be the future. LLMs will know enough to know that it should hand things off to something else.
        danielmarkbruce 2y ago
        It's the present. ChatGPT, for example, is an application. It uses models, but it does all kinds of stuff at the application level too.
        sdesol 2y ago
        Is the instructions to pass something off built into the model or is it clever prompting or a bit of both?
- CamperBob2 2y ago
  I know a great many people with PhDs. They're certainly not infallible by any means, but I can assure you, every single one of them can correctly count the number of occurrences of the letter 'r' in 'strawberry' if they put their mind to it. So can the current models. It's frustrating that so many people think this line of reasoning actually pays off in the long run, when talking about what AI models can and can't do. Got any other points that were right last month but wrong this month?
  - danielmarkbruce 2y ago
    There are always going to be doubters on this. It's like the self driving doubters. Until you get absolute perfection, they'll point out shortcomings. Never mind that humans have more holes than swiss cheese.
- visarga 2y ago
  Humans tasked to count how many vowels are in "Pneumonoultramicroscopicsilicovolcanoconiosis" (a real word), without seeing the word visually, just from language, would struggle. Working memory limits. We're not that different, we fail too.