3 ms·

Overall the industry needs more review, less hype. I was shocked to find out SWE-verified [0] is all but verified. [0] benchmark used by all major vendors to "

Overall the industry needs more review, less hype. I was shocked to find out SWE-verified [0] is all but verified. [0] benchmark used by all major vendors to "showcase" coding ability, turns out to be <10% properly solved: https://www.youtube.com/watch?v=QnOc_kKKuac https://www.youtube.com/watch?v=QnOc_kKKuac

belter 2y ago
Failure modes are also interesting to show what is happening or not really happening. Like the test of asking GenAI to create clocks at specific times, or people drawing with the left hand. All you get are clocks, at 10 min past two, or people drawing with the right hand, since it's 99% of what is in the training data. Like Sabine says, if the LLM models, already read all the Math books in the world but are not yet able to do basic math, without calling upon a calculator, how much reasoning is really emerging? "The Path to AGI is Coming Into View": https://youtu.be/mfbRHhOCgzs?t=219 https://youtu.be/mfbRHhOCgzs?t=219
- refulgentis 2y ago
  I'm not sure what Sabine means. It is a somewhat obvious category error, and fundamentally in error regardless. (I find it hard to believe that, for example, Sabine would beat an LLM on a randomly selection of 10 3x3 digit multiplication problem to be complicated in 60 seconds max, by either party)
- immibis 2y ago
  Or overflowing wine glasses.
- fragmede 2y ago
  numeracy isn't mathmatical reasoning
- omneity 2y ago
  I would offer an alternative, possible explanation. Having trained quite a few LLMs by now, especially around the “uplift” from a text completion model to an instruct one, I noticed that the instruction following capabilities tend not to be uniform across all the tasks the LLM is able to perform. In other words, it doesn’t know what is the implication of your request (time on the clock) in terms of change in output (the svg drawing of the clock), and all the abstractions and steps in between, this is why changing the time in the prompt might yield little difference. Instruct datasets are quite small in retrospect and hardly cover the range of tasks instruct LLMs are able to perform, and mostly rely on extrapolation (interpolation?) capabilities for the LLM from its small training set. And it is this generalization that is not evenly distributed. Just my two cents from my modest observations, sprinkle [citation needed] everywhere.

belter 2y ago

Failure modes are also interesting to show what is happening or not really happening. Like the test of asking GenAI to create clocks at specific times, or people drawing with the left hand. All you get are clocks, at 10 min past two, or people drawing with the right hand, since it's 99% of what is in the training data. Like Sabine says, if the LLM models, already read all the Math books in the world but are not yet able to do basic math, without calling upon a calculator, how much reasoning is really emerging? "The Path to AGI is Coming Into View": https://youtu.be/mfbRHhOCgzs?t=219 https://youtu.be/mfbRHhOCgzs?t=219

refulgentis 2y ago
I'm not sure what Sabine means. It is a somewhat obvious category error, and fundamentally in error regardless. (I find it hard to believe that, for example, Sabine would beat an LLM on a randomly selection of 10 3x3 digit multiplication problem to be complicated in 60 seconds max, by either party)
immibis 2y ago
Or overflowing wine glasses.
fragmede 2y ago
numeracy isn't mathmatical reasoning
omneity 2y ago
I would offer an alternative, possible explanation. Having trained quite a few LLMs by now, especially around the “uplift” from a text completion model to an instruct one, I noticed that the instruction following capabilities tend not to be uniform across all the tasks the LLM is able to perform. In other words, it doesn’t know what is the implication of your request (time on the clock) in terms of change in output (the svg drawing of the clock), and all the abstractions and steps in between, this is why changing the time in the prompt might yield little difference. Instruct datasets are quite small in retrospect and hardly cover the range of tasks instruct LLMs are able to perform, and mostly rely on extrapolation (interpolation?) capabilities for the LLM from its small training set. And it is this generalization that is not evenly distributed. Just my two cents from my modest observations, sprinkle [citation needed] everywhere.