---

name: pplm_attribute_objectives description: Compute PPLM-style bag-of-words or linear-classifier attribute losses and gradients for controlled generation.

PPLM Attribute Objectives

Use this skill when implementing or validating Plug and Play Language Model control objectives. It is appropriate when a fixed language model distribution must be steered toward a topic, sentiment, or toxicity-related attribute without changing model weights.

Inputs

• tokens: vocabulary strings aligned with logits.
• logits: numeric next-token logits or a perturbable logit proxy.
• target_words: bag-of-words target tokens, or classifier_weights for a linear classifier proxy.

Outputs

• Attribute loss where lower is better.
• Gradient over logits for minimization.
• Diagnostics including target probability mass.

Workflow

1. Convert logits to a stable softmax distribution.
2. For bag-of-words control, sum probability mass assigned to target words.
3. Return -log(target_mass) as the loss and the analytic gradient with respect to logits.
4. Treat missing target words as an explicit contract error rather than silently succeeding.

Validation

Run python tests/test_attribute_objectives.py from this skill directory, or validate through the Distiller skill-tree validator.

Limitations

This deterministic helper validates the PPLM objective interface; it is not a full neural discriminator or GPT-2 forward pass.