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This model like its predecessor still lacks the ability to infer within constraints of physical world [1]. It does not have any notion of what a physical world
by freediver 5y ago
This model like its predecessor still lacks the ability to infer within constraints of physical world [1]. It does not have any notion of what a physical world is, and still is, at the end of the day, a statistical model.
Attempting to do recreate the entirety of the physical world by describing it in text is an impressive feat, but a futile attempt.
It is prone to inference errors that the model itself is not aware of. There are less and less of such errors, but the very inability to tell that what you are outputting is gibberish is one of the biggest problems of these models. In other words, if the model could simply say 'I do not know' or 'I am not sure' for every occasion when it is not 100% sure in something (like a human can), this would drastically improve the usefulness.
[1] Spent a lot of time pondering on this topic https://arxiv.org/pdf/1906.01873.pdf https://arxiv.org/pdf/1906.01873.pdf
- buddhistdude 5y agoYour mom’s a statistical model
- phreeza 5y ago> 'I do not know' or 'I am not sure' for every occasion when it is not 100% sure in something (like a human can), this would drastically improve the usefulness. This is exactly what a language model does though, just at a different level of abstraction. It gives you a probability distribution over tokens at each step. That distribution can be narrow (low entropy, certain) or wide (high entropy, uncertain). The language output you see is just a sampling at some temperature from these distributions. Though glancing at your paper I assume you are aware of this and I am missing the point you are making?
- freediver 5y agoStatistical approaches require you to most commonly use a threshold. Sometimes the model output can be above the threshold and still wrong, and below the threshold and correct. You can never tell for sure, but just try to improve the benchmark average. This is not acceptable in most use cases where the wrong outcome of a single output can be disastrous. When a human does not know something it can tell that with 100% certainty.
- phreeza 5y agoI don't see the difference between a human and a statistical model here. Surely in order to select an action to take, a person also has to apply some sort to threshold on their confidence? E.g. how is a doctor deciding to amputate or not amputate an organ based on an x-ray different from a classification model for the same task? That problem aside, language models like Gopher are in fact generative, so no such threshold is needed! You instead sample from the implicit distribution.
- freediver 5y agoThe correct analogy would be if I ask you when did Neil Armstrong land on Mars and you 100% know 'never'. A statistical model may output '1969' with 10% confidence and/or '2147' with 3% confidence.
- leobg 5y agoExactly! Same problem with GPT-3. That’s why most of the commercial applications people have found for it so far are in marketing. Even if you tell it explicitly not to make up stuff and only chose from applicable answers, it will confabule. And you never know when it does.
- vagab0nd 5y ago> This model like its predecessor still lacks the ability to infer within constraints of physical world [1]. It does not have any notion of what a physical world is, and still is, at the end of the day, a statistical model. How did you reach this conclusion? Is there evidence or was it inferred from the model structure?