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I'm not sure I would measure LLMs on recommending a fairly obscure and rather new spec that isn't even fully supported by e.g. Chrome. That's a leap that I, a h

I'm not sure I would measure LLMs on recommending a fairly obscure and rather new spec that isn't even fully supported by e.g. Chrome. That's a leap that I, a human, wouldn't have made either.

mandevil 1y ago
On the other hand, I find that "someone who has read all the blogs and papers and so can suggest something you might have missed" is my favorite use case for LLM, so seeing that it can miss a useful (and novel to me) idea is annoying.
- Hackbraten 1y ago
  Which is easier for you to remember: facts you’ve seen a hundred times in your lifetime, or facts you’ve seen once or twice? For an LLM, I’d expect it to be similar. It can recall the stuff it’s seen thousands of times, but has a hard time recalling the niche/underdocumented stuff that it’s seen just a dozen times.
  - gtsop 1y ago
    > Which is easier for you to remember: facts you’ve seen a hundred times in your lifetime, or facts you’ve seen once or twice? The human brain isn't a statistical aggregator. If you see a psychologically socking thing once in your lifetime, you might remember it even after dementia hits when you're old. On the other hand, you pass by hundrends of shops every day and receive the data signal of their signs over and over and over, yet you remember nothing. You remeber stuff you pay attention to (for whatever reason)
gtsop 1y ago
So is the expectation for it to suggest obvious solutions that the majority of people already know? I am fine with this, but let's be clear about what we're expecting
- jeremyjh 1y ago
  All it can do is predict the next token. So, yes.
  - danielbln 1y ago
    If it's still 2020, then yes. In 2025 post-training like RLHF made it that these models do not just predict the next token, the reward function is a lot more involved than that.
    - jeremyjh 1y ago
      Instruct models like ChatGPT are still token predictors. Instruction following is an emergent behavior from fine-tuning and reward modeling layered on top of the same core mechanism: autoregressive next-token prediction.
- pdabbadabba 1y ago
  > So is the expectation for it to suggest obvious solutions that the majority of people already know? Certainly a majority of people don't know this. What we're really asking is whether an LLM is expected to more than (or as much as) the average domain expert.

I'm not sure I would measure LLMs on recommending a fairly obscure and rather new spec that isn't even fully supported by e.g. Chrome. That's a leap that I, a human, wouldn't have made either.

mandevil 1y ago

On the other hand, I find that "someone who has read all the blogs and papers and so can suggest something you might have missed" is my favorite use case for LLM, so seeing that it can miss a useful (and novel to me) idea is annoying.

Hackbraten 1y ago
Which is easier for you to remember: facts you’ve seen a hundred times in your lifetime, or facts you’ve seen once or twice? For an LLM, I’d expect it to be similar. It can recall the stuff it’s seen thousands of times, but has a hard time recalling the niche/underdocumented stuff that it’s seen just a dozen times.
- gtsop 1y ago
  > Which is easier for you to remember: facts you’ve seen a hundred times in your lifetime, or facts you’ve seen once or twice? The human brain isn't a statistical aggregator. If you see a psychologically socking thing once in your lifetime, you might remember it even after dementia hits when you're old. On the other hand, you pass by hundrends of shops every day and receive the data signal of their signs over and over and over, yet you remember nothing. You remeber stuff you pay attention to (for whatever reason)

gtsop 1y ago

So is the expectation for it to suggest obvious solutions that the majority of people already know? I am fine with this, but let's be clear about what we're expecting

jeremyjh 1y ago
All it can do is predict the next token. So, yes.
- danielbln 1y ago
  If it's still 2020, then yes. In 2025 post-training like RLHF made it that these models do not just predict the next token, the reward function is a lot more involved than that.
  - jeremyjh 1y ago
    Instruct models like ChatGPT are still token predictors. Instruction following is an emergent behavior from fine-tuning and reward modeling layered on top of the same core mechanism: autoregressive next-token prediction.
pdabbadabba 1y ago
> So is the expectation for it to suggest obvious solutions that the majority of people already know? Certainly a majority of people don't know this. What we're really asking is whether an LLM is expected to more than (or as much as) the average domain expert.