transformers/examples/pplm

PPLM

This folder contains the original code used to run the Plug and Play Language Model (PPLM).

Plug and Play Language Models: a Simple Approach to Steerable Text Generation

Authors: Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu

PPLM allows a user to flexibly plug in one or more tiny attribute models representing the desired steering objective into a large, unconditional LM. The method has the key property that it uses the LM as is---no training or fine-tuning is required---which enables researchers to leverage best-in-class LMs even if they do not have the extensive hardware required to train them.

Paper link:

Blog link: https://eng.uber.com/pplm

Setup

git clone https://github.com/huggingface/transformers && cd transformers
pip install [--editable] .
pip install nltk torchtext # additional requirements.
cd examples/pplm

PPLM-BoW

Example command for bag-of-words control

python run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 1 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95

Tuning hyperparameters for bag-of-words control

1. Increase --stepsize to intensify topic control, and decrease its value to soften the control. --stepsize 0 recovers the original uncontrolled GPT-2 model.

2. If the language being generated is repetitive (For e.g. "science science experiment experiment"), there are several options to consider:
   a) Reduce the --stepsize
   b) Increase --kl_scale (the KL-loss coefficient) or decrease --gm_scale (the gm-scaling term)
   c) Add --grad-length xx where xx is an (integer <= length, e.g. --grad-length 30).
   

PPLM-Discrim

Example command for discriminator based sentiment control

python run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 10 --num_samples 1 --stepsize 0.03 --kl_scale 0.01 --gm_scale 0.95

Tuning hyperparameters for discriminator control

1. Increase --stepsize to intensify topic control, and decrease its value to soften the control. --stepsize 0 recovers the original uncontrolled GPT-2 model.

2. Use --class_label 3 for negative, and --class_label 2 for positive

Example command for detoxificiation:

python run_pplm.py -D toxicity --length 100 --num_iterations 10 --cond-text 'TH PEOPLEMan goddreams Blacks' --gamma 1.0 --num_samples 10 --stepsize 0.02