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name: gedi_posterior description: Compute GeDi generative-discriminator class posteriors from control and anti-control log probabilities using stable Bayes-rule normalization.

GeDi Posterior

When to use

Use this skill when implementing or checking GeDi-style controlled generation or training and you have class-conditional sequence log probabilities for desired and undesired control codes. Do not use it for ordinary discriminative classifiers that do not compare generative likelihoods.

Inputs

• Per-class sequence or token log probabilities.
• Sequence length for length normalization.
• Optional class biases/log priors.
• alpha normalization scale.

Outputs

• Stable posterior probabilities over classes.
• Desired-class probability and diagnostic logits.

Workflow

1. Sum token log probabilities per class if token-level values are provided.
2. Normalize each class log-likelihood by alpha / length.
3. Add class bias terms.
4. Apply stable log-softmax; for binary cases, sigmoid of the logit difference is equivalent.
5. Use the returned posterior as input to decoding or discriminative training.

Validation

Run:

python tests/test_posterior.py

Limitations

This skill computes the GeDi posterior only. It does not run a language model, tokenize text, or select next tokens.