I'm a staunch believer that it would be foolish to rely on GPT4 for quality comparisons, and it has been mind boggling to see so many people do it and treat it as perfect proof of anything.
It would be slightly more understandable if there was a study to see how human and gpt4 preferences compare, but I'm unaware of any such thing.
I wound up abandoning the experiment because getting enough reliability would have required screwing down the standards so tightly that it would have ruined the underlying point of the thing.
I've done a lot of work in audio synthesis, which is notoriously difficult measure. The gold-standard is human ratings of audio quality, but it is tough to design good tests (easy to fatigue raters) and the iteration time waiting for results is quite long.
Instead, there's now some projects which use neural networks trained on human ratings to predict audio quality, such as ViSQoL: https://github.com/google/visqol
This opens up fast iteration - scores going up generally corresponds to higher quality - followed by human testing at major milestones (eg, releasing a paper/model). VISQOL has a harder time comparing 'unrelated' models, IMO - ends up being not so great for comparison of different techniques, but excellent for measuring incremental improvement or catching regressions.
But, in the end, yes - you can use NN's to measure the quality of other NN's, so long as you're careful about it and make use of human raters from time to time as well.
The problem of test data getting into the training data seems to be an especially pernicious issue with LLM's, which isn't really arising in the audio synthesis space.
AudioLM puts a language model on top of the compression tokens, and thus can generate speech or other audio. There's piles of recent papers pushing that approach into music generation. Mulan is a name that comes to mind.
Or maybe your interested in audio separation to get at the isolated instruments? There's lots of great work on that, as well. Like MixIT, which is an unsupervised audio separation system.
I even read a classic book the other day and had a great discussion with ChatGPT about moral relativism, the different schools of thought and how it fit into philosophy as a whole. For students this technology is incredible, I wish I had it for all my classes.
Even sometimes comments I'll make on here or Reddit I'll pass through ChatGPT first to see if I made any mistakes in my logic.
There is no such thing as an average person.
https://www.thestar.com/news/insight/when-u-s-air-force-disc...
For whatever reason*, it is particularly bad at discussing philosophy I find. When I was grading philosophy 101, I would have probably given it a passing grade against the overall curve, but that's about it. Philosophy is a discipline of careful, sometimes jargoney, and always very couched assertions that can be easily misunderstood and appropriated. This is probably its greatest weakness, and in many ways this weakness is the progenitor of philosophy itself in the Western world, with Plato at the start (i.e. with the figure of the sophist, the paradox of a false wisdom).
- Maybe one reason: there is a huge amount of, lets say, "armchair philosophy" on the internet, compared to other disciplines. Many blogposts and tiny manifestos of people really excited by some out of context quote from Spinoza or whatever. And you start to really feel this part of the dataset when you ask about philosophy. Many strange takes and misunderstandings.
Philosophy
Ethics/Moral Philosophy
Meta-Ethics: The study of moral thought, language, and properties
Moral Realism: Belief that there are objective moral facts
Moral Anti-Realism: Denial of the existence of objective moral facts
Moral Relativism: The belief that moral judgments are true or false only relative to some particular standpoint
I don't doubt it can do, like, Wikipedia type classification ok, but that's not like really getting to the substance of anything! And, either way, its not like there is one decided-upon hierarchy of concepts like this people consciously work within. This is a fine picture to some, but others might contest, perhaps, that Meta-Ethics is the "study of moral thought, language, and properties." What is "moral language" anyway? Why is it meta relative to Moral Philosophy writ-large? Or perhaps one might argue that we need to think of meta-ethics as a sibling rather than child. The whole discipline is a mess of different thoughts and possible rebuttals and grand intellectual overturnings that will not be captured here. Maybe just try pasting that back into the prompt and asking "what's wrong with this picture?".
But like I said, its fine in that its fairly comparable to Wikipedia for utility, (with IMO a worse interface, but I get why people like it more).
After all, LLMs and ChatGPT in particular are are indistinguishable from productised Gell-Mann Amnesia.
Edit: rewrote to be more neutral, sorry
Keep in mind that due to the nature of the data and the RLHF training, it's more like a weighted average, something like
0.5*(average American view) + 0.4*(average WEIRD view) + 0.1*(average human view).
This may or may not matter depending on the questions you're asking, just something to keep in mind.
The world has long been divided into two camps: People who think computers can make mistakes; and people who think computers never make mistakes, and blame the humans that program them.
Well, now the computers are programming themselves. And clearly they're making mistakes.