uber-research/PPLM

Health v2 · maintenance only

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1153 stars · 203 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PPLM (Plug and Play Language Model) is a research implementation for controlled text generation that steers the topic and attributes of GPT-2 outputs using small attribute models, without training or fine-tuning the base language model. It was published by Uber AI at ICLR 2020 and is also integrated into Hugging Face Transformers.

Use cases

• steer GPT-2 text generation toward a specific topic
• control attributes of generated text without fine-tuning the language model
• run bag-of-words topic-controlled generation with run_pplm.py
• use PPLM as a baseline for controlled text generation research
• experiment with discriminator-based attribute steering of language models
• try controlled text generation in a Colab notebook without setup

When to choose

• you need topic- or attribute-controlled generation from GPT-2 without retraining
• you are reproducing the PPLM paper or using it as a research baseline
• you want to plug in small attribute models into a frozen language model

When to avoid

• you need controlled generation with modern LLMs beyond GPT-2
• you want a production-ready, actively maintained text generation library
• you prefer fine-tuning or prompt-based steering approaches instead of gradient-based control

Facets

library · maturity maintenance

nlp machine-learning llm-inference machine-learning deep-learning large-language-models python controlled-text-generation gpt-2 attribute-steering research-code language-models natural-language-processing

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