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name: pplm_attribute_models description: Use when a recovery or implementation needs differentiable PPLM attribute scores for BoW or simple discriminator-style control.

PPLM Attribute Models

Use this skill to build the plug-in attribute model p(a|x) used by PPLM. Do not use it to modify base LM weights.

Inputs

• Vocabulary tokens.
• Topic BoW terms or linear classifier weights.
• Candidate token probabilities or a hidden vector.

Outputs

• Numeric attribute score.
• Attribute loss suitable for gradient ascent/descent in a controller.
• Diagnostics for missing BoW terms.

Workflow

1. Normalize vocabulary and target words.
2. For BoW control, score the probability mass assigned to target words using -log(sum p(target)) as a minimization loss.
3. For discriminator-style control, apply a linear score or softmax outside the base LM.
4. Return diagnostics; never update the base LM.

Validation

Run python tests/test_attribute_models.py.

Limitations

Reduced proxy implementations must declare that they are not full GPT-2 345M reproduction.