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BLOOM isn’t as good as GPT-3 because it doesn’t use as much training data. LLM quality is still data bound [0]. Further limiting data by requiring annotation is

by rcme 4y ago

BLOOM isn’t as good as GPT-3 because it doesn’t use as much training data. LLM quality is still data bound [0]. Further limiting data by requiring annotation is not going to work, at least with the current LLM modeling approach. 0: https://www.alignmentforum.org/posts/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implications https://www.alignmentforum.org/posts/6Fpvch8RR29qLEWNH/chinc...

CuriouslyC 4y ago
As the scale of input data goes up linearly, the scale of commonly observed input patterns goes up logarithmically. If we bumped the scale up an order of magnitude in terms of common input tokens, that still means we could annotate the important part of a 150TB text corpus for 125B worth of human annotation. Given that could break the budget of even large corporations, realistically we'd probably train a model to predict the scores of interest using a fraction of that much human annotation, which would be inferior but still a massive improvement. It is also likely that corporations would team up with indirect competitors to share the cost of annotation and gain an advantage against direct competitors.
- rcme 4y ago
  How do you figure? Let's say a commonly observed input pattern comprises 1% of training data. For a data set of size N, 0.01 * N examples will contain the pattern. If we increase the size to 2N, 0.01 * 2 * N examples will contain the pattern. Why is the growth logarithmic?
  - CuriouslyC 4y ago
    As the data set size increases, the average frequency of non-trivial most frequent patterns will go down, and the tail will get much larger. Thus if you had a 1% cutoff, the percentage of the data set hitting this cutoff goes down as the data set size goes up. Take a look at pareto distributions with high alpha to understand the statistics of it. Of course, this is only true if new data is distinct from old data. If you just copied your data set 10x and pretended it was a 10x larger data set, it would behave like you expect.
    - rcme 4y ago
      Hmm I’m still not convinced. Gather training data can be thought of sampling the underlying distribution of the data. In that sense, you’d expect the proportions of things to converge towards the underlying distribution as you gather more data.
      - CuriouslyC 4y ago
        That would be true if we were sampling from the underlying distribution in an unbiased and balanced way from the beginning. Instead data is generated and incorporated one set at a time, and each set is biased. Jargon and terms vary, but the language plumbing is the same - new sets bolster common phrases/idioms and lengthen the tail with specific tokens. Keep in mind though, language isn't a stationary process.
        rcme 4y ago
        Even if each dataset is biased, I’m not still not sure how you derived logarithmic growth from the general notion of bias in data. For instance, assuming the data is biased, perhaps it is biased in the other direction and contains more common patterns compared to the underlying distribution.
        CuriouslyC 4y ago
        There is a lot to this subject, it might be easier if you took a look at https://martinapugliese.github.io/data/heaps-law-languages/ https://martinapugliese.github.io/data/heaps-law-languages/. Note that when plotting corpus size vs unique words, the log plot is expected to be linear.
        rcme 4y ago
        Ah, I see what you mean: the number of unique examples increases logarithmicly with data size, which kind of makes sense. Language, in this case, follows a power law. I think you argument is that this means smaller datasets are ok because they contain "most" of what the larger datasets contains. But I think this data-power-rule implies the opposite. ML models can often get to 80-90% accuracy on some task. Unfortunately, these models often aren't that useful because that missing 10% of accuracy matters a lot to users. So what this data-power-rule implies is that, in order to get the last 10% of gains, you need 10x the amount of data.

by rcme 4y ago

CuriouslyC 4y ago

As the scale of input data goes up linearly, the scale of commonly observed input patterns goes up logarithmically. If we bumped the scale up an order of magnitude in terms of common input tokens, that still means we could annotate the important part of a 150TB text corpus for 125B worth of human annotation. Given that could break the budget of even large corporations, realistically we'd probably train a model to predict the scores of interest using a fraction of that much human annotation, which would be inferior but still a massive improvement. It is also likely that corporations would team up with indirect competitors to share the cost of annotation and gain an advantage against direct competitors.

rcme 4y ago
How do you figure? Let's say a commonly observed input pattern comprises 1% of training data. For a data set of size N, 0.01 * N examples will contain the pattern. If we increase the size to 2N, 0.01 * 2 * N examples will contain the pattern. Why is the growth logarithmic?
- CuriouslyC 4y ago
  As the data set size increases, the average frequency of non-trivial most frequent patterns will go down, and the tail will get much larger. Thus if you had a 1% cutoff, the percentage of the data set hitting this cutoff goes down as the data set size goes up. Take a look at pareto distributions with high alpha to understand the statistics of it. Of course, this is only true if new data is distinct from old data. If you just copied your data set 10x and pretended it was a 10x larger data set, it would behave like you expect.
  - rcme 4y ago
    Hmm I’m still not convinced. Gather training data can be thought of sampling the underlying distribution of the data. In that sense, you’d expect the proportions of things to converge towards the underlying distribution as you gather more data.
    - CuriouslyC 4y ago
      That would be true if we were sampling from the underlying distribution in an unbiased and balanced way from the beginning. Instead data is generated and incorporated one set at a time, and each set is biased. Jargon and terms vary, but the language plumbing is the same - new sets bolster common phrases/idioms and lengthen the tail with specific tokens. Keep in mind though, language isn't a stationary process.
      - rcme 4y ago
        Even if each dataset is biased, I’m not still not sure how you derived logarithmic growth from the general notion of bias in data. For instance, assuming the data is biased, perhaps it is biased in the other direction and contains more common patterns compared to the underlying distribution.
        CuriouslyC 4y ago
        There is a lot to this subject, it might be easier if you took a look at https://martinapugliese.github.io/data/heaps-law-languages/ https://martinapugliese.github.io/data/heaps-law-languages/. Note that when plotting corpus size vs unique words, the log plot is expected to be linear.
        rcme 4y ago
        Ah, I see what you mean: the number of unique examples increases logarithmicly with data size, which kind of makes sense. Language, in this case, follows a power law. I think you argument is that this means smaller datasets are ok because they contain "most" of what the larger datasets contains. But I think this data-power-rule implies the opposite. ML models can often get to 80-90% accuracy on some task. Unfortunately, these models often aren't that useful because that missing 10% of accuracy matters a lot to users. So what this data-power-rule implies is that, in order to get the last 10% of gains, you need 10x the amount of data.