4 ms·

I doubt it would take very many suitable examples in the training dataset to fix problems like that.

by _ea1k 3y ago

panarky 3y ago
I think this problem is mostly already solved with more capable models. Here's the shoe-identification experiment with GPT-4 and it's not confused by a suit jacket: https://imgur.com/a/cPP6pH7 https://imgur.com/a/cPP6pH7 It's also not confused by a tuxedo: https://imgur.com/a/mWMxZbA https://imgur.com/a/mWMxZbA Also tried the same examples with Google Gemini 1.5 Pro and it doesn't hallucinate either. https://imgur.com/a/3Yv7wuP https://imgur.com/a/3Yv7wuP
PaulHoule 3y ago
If it sees 90% of photos of basketball players wearing sneakers it is still going to get the idea that basketball players wear sneakers. I guess I could make up some synthetic data where I mix and match people's shoes but it's a strange way to get at the problem: train on some image set that has completely unrealistic statistics to try to make it pay attention to particular features as opposed to other ones. Problems like that have been bugging me for a while, if you had some Bayesian model you could adjust the priors to make the statistics match a particular set of cases and it would be nice if you could do the same with a wide range of machine learning approaches. For instance you might find that 90% of the cases are "easy" cases that seem like a waste to include in the training data, keeping there gives the model the right idea about the statistics but may burn up the learning capacity of the model such that it can't really learn from the other 10%. I talked w/ some contractors who made intelligence models for three-letter agencies and they told me all about ways for dealing with that come down to building multiple-staged models where you build a model that separates the hard cases from the easy cases with specialized training sets for each one. It's one of those things that some people have forgotten in the Research Triangle Park area but Silicon Valley never knew.

I doubt it would take very many suitable examples in the training dataset to fix problems like that.

by _ea1k 3y ago

I doubt it would take very many suitable examples in the training dataset to fix problems like that.

I think this problem is mostly already solved with more capable models. Here's the shoe-identification experiment with GPT-4 and it's not confused by a suit jacket: https://imgur.com/a/cPP6pH7 https://imgur.com/a/cPP6pH7 It's also not confused by a tuxedo: https://imgur.com/a/mWMxZbA https://imgur.com/a/mWMxZbA Also tried the same examples with Google Gemini 1.5 Pro and it doesn't hallucinate either. https://imgur.com/a/3Yv7wuP https://imgur.com/a/3Yv7wuP

PaulHoule 3y ago

If it sees 90% of photos of basketball players wearing sneakers it is still going to get the idea that basketball players wear sneakers. I guess I could make up some synthetic data where I mix and match people's shoes but it's a strange way to get at the problem: train on some image set that has completely unrealistic statistics to try to make it pay attention to particular features as opposed to other ones. Problems like that have been bugging me for a while, if you had some Bayesian model you could adjust the priors to make the statistics match a particular set of cases and it would be nice if you could do the same with a wide range of machine learning approaches. For instance you might find that 90% of the cases are "easy" cases that seem like a waste to include in the training data, keeping there gives the model the right idea about the statistics but may burn up the learning capacity of the model such that it can't really learn from the other 10%. I talked w/ some contractors who made intelligence models for three-letter agencies and they told me all about ways for dealing with that come down to building multiple-staged models where you build a model that separates the hard cases from the easy cases with specialized training sets for each one. It's one of those things that some people have forgotten in the Research Triangle Park area but Silicon Valley never knew.