PPLM (Plug and Play Language Model) offers a method for controlled text generation by steering large, unconditional language models (LMs) towards specific topics or attributes without requiring LM fine-tuning. This approach benefits researchers and developers who want to leverage state-of-the-art LMs without the substantial computational resources needed for training.

How It Works

PPLM integrates small, pre-trained attribute models (e.g., bag-of-words or discriminators) with an existing LM. It uses a gradient-based approach to modify the LM's internal states, guiding the generation process towards the desired attributes. This method preserves the original LM's capabilities while enabling flexible control, making it advantageous for targeted text generation tasks.

Quick Start & Requirements

• Install via pip install -r requirements.txt.
• Requires Python and standard ML libraries.
• Demo and Colab notebook available for setup-free experimentation.

Highlighted Details

• Enables steering of GPT-2 models.
• Supports control via bag-of-words or discriminator models.
• Offers hyperparameter tuning for fine-grained control over generation.
• Integrated into the 🤗/Transformers repository.

Maintenance & Community

The project is associated with Uber AI Labs. Further community engagement details are not explicitly provided in the README.

Licensing & Compatibility

The repository does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The README notes that hyperparameters for models in the main directory and 🤗/Transformers may differ from those in the original paper by a factor of 5. Specific code and models corresponding to the paper's analysis are available in a separate linked location.