Scaling Language Models: Methods, Analysis & Insights from Training Gopher(Jack W. Rae et al) (summarized by Rohin): This paper details the training of the Gopher family of large language models (LLMs), the biggest of which is named Gopher and has 280 billion parameters. The algorithmic details are very similar to the GPT series (AN #102): a Transformer architecture trained on next-word prediction. The models are trained on a new data distribution that still consists of text from the Internet but in different proportions (for example, book data is 27% of Gopher’s training data but only 16% of GPT-3’s training data).
Like other LLM papers, there are tons of evaluations of Gopher on various tasks, only some of which I’m going to cover here. One headline number is that Gopher beat the state of the art (SOTA) at the time on 100 out of 124 evaluation tasks.
The most interesting aspect of the paper (to me) is that the entire Gopher family of models were all trained on the same number of tokens, thus allowing us to study the effect of scaling up model parameters (and thus training compute) while holding data constant. Some of the largest benefits of scale were seen in the Medicine, Science, Technology, Social Sciences, and the Humanities task categories, while scale has not much effect or even a negative effect in the Maths, Logical Reasoning, and Common Sense categories. Surprisingly, we see improved performance on TruthfulQA (AN #165) with scale, even though the TruthfulQA benchmark was designed to show worse performance with increased scale.
We can use Gopher in a dialogue setting by prompting it appropriately. The prompt specifically instructs Gopher to be “respectful, polite, and inclusive”; it turns out that this significantly helps with toxicity. In particular, for the vanilla Gopher model family, with more scale the models produce more toxic continuations given toxic user statements; this no longer happens with Dialogue-Prompted Gopher models, which show slight reductions in toxicity with scale in the same setting. The authors speculate that while increased scale leads to an increased ability to mimic the style of a user statement, this is compensated for by an increased ability to account for the prompt.
Another alternative the authors explore is to finetune Gopher on 5 billion tokens of dialogue to produce Dialogue-Tuned Gopher. Interestingly, human raters were indifferent between Dialogue-Prompted Gopher and Dialogue-Tuned Gopher.
Training Compute-Optimal Large Language Models(Jordan Hoffmann et al) (summarized by Rohin): One application of scaling laws (AN #87) is to figure out how big a model to train, on how much data, given some compute budget. This paper performs a more systematic study than the original paper and finds that existing models are significantly overtrained. Chinchilla is a new model built with this insight: it has 4x fewer parameters than Gopher, but is trained on 4x as much data. Despite using the same amount of training compute as Gopher (and lower inference compute), Chinchilla outperforms Gopher across a wide variety of metrics, validating these new scaling laws.
You can safely skip to the opinion at this point – the rest of this summary is quantitative details.
We want to find functions N(C) and D(C) that specify the optimal number of parameters N and the amount of data D to use given some compute budget C. We’ll assume that these scale with a power of C, that is, N(C) = k_N * C^a and D(C) = k_D * C^b, for some constants a, b, k_N, and k_D. Note that since total compute increases linearly with both N (since each forward / backward pass is linear in N) and D (since the number of forward / backwards passes is linear in D), we need to have a + b = 1. (You can see this somewhat more formally by noting that we have C = k_C * N(C) * D(C) for some constant k_C, and then substituting in the definitions of N(C) and D(C).)
This paper uses three different approaches to get three estimates of a and b. The approach I like best is “isoFLOP curves”:
1. Choose a variety of possible values of (N, D, C), train models with those values, and record the final loss obtained. Note that not all values of (N, D, C) are possible: given any two values the third is determined.
2. Draw isoFLOP curves: for each value of C, choose either N or D to be your remaining independent variable, and fit a parabola to the losses of the remaining points. The minimum of this parabola gives you an estimate for the optimal N and D for each particular value of C.
3. Use the optimal (N, D, C) points to fit N(C) and D(C).
This approach gives an estimate of a = 0.49; the other approaches give estimates of a = 0.5 and a = 0.46. If we take the nice round number a = b = 0.5, this suggests that you should scale up parameters and data equally. With 10x the computation, you should train a 3.2x larger model with 3.2x as much data. In contrast, the <a href= "https://rohinshah.us18.list-manage.com/track/click?u=1d1821210cc4f04d1e05c4fa6&id=220de05b39&e=a708728e18" target="_blank" rel="noopener" data-saferedirecturl= "https://www.google.com/url?q=https://rohinshah.us18.list-manage.com/track/click?u%3D1d1821210cc4f04d1e05c4fa6%26id%3
