Controlling GPT-3 with Logit Bias | Hacker News Reader

Controlling GPT-3 with Logit Bias

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Not sure if useful - but if you know the initial biases of outputs, you can recalibrate these yourselves, provided you have output probabilities for all tokens (or all non-negligible ones at least).

Say the model outputs n tokens, and the prior (bias) in the model for tokens is m = (m_1, m_2, .... m_n), and the new prior you want is n=(n_1, n_2, ..._)

Then if the model outputs prediction p = (p_1, ... , p_n) for all tokens, then the new output you are looking for is

bias_shift(p) = softmax(logit(p) + log(n) - log(m))

You can prove this using Bayes rule + a bit of algebra. Most ML people don't seem to know this trick, but it's super useful for domain adaptation / class upsampling, where you know that the class balance in your training set is different to the one you want to predict on.

This is a great tip, thanks! Is this essentially importance sampling? Reminds me of a technique for off-policy RL.

They are related, but not the same. This recalibrating of logits is basically what you would do afterwards, to correct for the fact that you have been importance sampling your replay buffer.

This is like the density ratio approach (https://arxiv.org/abs/2002.11268). The Hybrid Autoregressive Transducer also uses a similar idea (https://arxiv.org/abs/2003.07705).

Do you really need the renormalization, i.e. the outer softmax?

We logit to start so we need to go back from logit space -> probability space. Linear transformations in logit space don't preserve the sum = 1 requirement of the corresponding probability so you can't just take the sigmoid of each element individually. This is just a slick way of writing it, you can write it all out without any logits / sigmoids, but it's easier to do the algebra in logit space.

Sorry, maybe my question was misleading. Why do you need the sum=1? See the linked papers. They do the same but without the renormalization.

Ah ok, I'm not overly familiar with the problem they're trying to solve really - wouldn't always be necessary depending on your application, but I was talking in broad ML terms, if you need a calibrated prediction at the end you do.

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