6 ms·

The problem is OA also specified the level of cherrypicking, including sometimes picking the very first one. Do any of the conditional samples, picking out of 1

by gwern 7y ago

The problem is OA also specified the level of cherrypicking, including sometimes picking the very first one. Do any of the conditional samples, picking out of 1 or 5, look like the OA blogpost ones? I didn't read them all, but they didn't look like it. Or much better than the 345M recently released, for that matter.

YeGoblynQueenne 7y ago
I don't know why it is assumed that eyballing text generated by a neural net can give an accurate measure of the network's quality. Evaluating this sort of output is difficult and there are no good metrics for it. Then of course there is the fact that the network can generate text a lot faster than a human can read it. Think for a moment how many distinct passages these systems can generate -possibly even infinite- and how few of them a single person can realistically hope to read. It's very hard to know if the few passages one ends up reading are representative of the output of the network, or not. It'd be even harder to compare two systems just by eyballing their output side-by-side.
- gwern 7y ago
  You have to eyeball it because (a) 'what it looks like to a human eyeballing it' is the most important metric for misuse - no one cares that much about a model which has a slightly better log loss but a human can instantly spot is robospam, that can't be abused or used for fun projects like generating poetry; and (b) the log loss between GPT-2 and his will not be comparable due to the different training corpuses (and possibly architectural differences as well), so the actual metrics are not useful. Given the large difference in quality, it only takes a few comparisons. Again, just look at the samples. Read a few. Do they really look the same in coherency and quality and realism?
  - YeGoblynQueenne 7y ago
    Do you think that if you were given a sample of text genereated by one of the two systems you would be able to tell which system generated it? Looking at two texts side-by-side while knowing which is which may well create an illusion of striking differences, which however are not really there. This is why concrete metrics are necessary- because human judgement is biased and inconsistent. But, like you say- there are no good metrics. In fact, the only thing we have to go by when discussing Open AI's model in the first place is opinions and convictions. Not least the opinion of OpenAi that their model generates text of unprecedented coherence, which itself is not based on anything else than eyballing.
    - gwern 7y ago
      I think I could. Few or none of the new samples even manage to maintain coherency in a sample, and you can see all sorts of garbage prose being generated.
      - YeGoblynQueenne 7y ago
        Perhaps you could. Personally, I think it's impossible to get an accurate comparison in this way.
        gwern 7y ago
        I think it is possible, because it turns out I am right: the quality is much lower when compared directly on perplexity as well: https://medium.com/@NPCollapse/addendum-evaluation-of-my-model-e6734b51a830 https://medium.com/@NPCollapse/addendum-evaluation-of-my-mod...

by gwern 7y ago

YeGoblynQueenne 7y ago

I don't know why it is assumed that eyballing text generated by a neural net can give an accurate measure of the network's quality. Evaluating this sort of output is difficult and there are no good metrics for it. Then of course there is the fact that the network can generate text a lot faster than a human can read it. Think for a moment how many distinct passages these systems can generate -possibly even infinite- and how few of them a single person can realistically hope to read. It's very hard to know if the few passages one ends up reading are representative of the output of the network, or not. It'd be even harder to compare two systems just by eyballing their output side-by-side.

gwern 7y ago
You have to eyeball it because (a) 'what it looks like to a human eyeballing it' is the most important metric for misuse - no one cares that much about a model which has a slightly better log loss but a human can instantly spot is robospam, that can't be abused or used for fun projects like generating poetry; and (b) the log loss between GPT-2 and his will not be comparable due to the different training corpuses (and possibly architectural differences as well), so the actual metrics are not useful. Given the large difference in quality, it only takes a few comparisons. Again, just look at the samples. Read a few. Do they really look the same in coherency and quality and realism?
- YeGoblynQueenne 7y ago
  Do you think that if you were given a sample of text genereated by one of the two systems you would be able to tell which system generated it? Looking at two texts side-by-side while knowing which is which may well create an illusion of striking differences, which however are not really there. This is why concrete metrics are necessary- because human judgement is biased and inconsistent. But, like you say- there are no good metrics. In fact, the only thing we have to go by when discussing Open AI's model in the first place is opinions and convictions. Not least the opinion of OpenAi that their model generates text of unprecedented coherence, which itself is not based on anything else than eyballing.
  - gwern 7y ago
    I think I could. Few or none of the new samples even manage to maintain coherency in a sample, and you can see all sorts of garbage prose being generated.
    - YeGoblynQueenne 7y ago
      Perhaps you could. Personally, I think it's impossible to get an accurate comparison in this way.
      - gwern 7y ago
        I think it is possible, because it turns out I am right: the quality is much lower when compared directly on perplexity as well: https://medium.com/@NPCollapse/addendum-evaluation-of-my-model-e6734b51a830 https://medium.com/@NPCollapse/addendum-evaluation-of-my-mod...