Steer GPT-2

PPLM

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

It has also been integrated into the 🤗/Transformers repository.

Plug and Play Language Models: a Simple Approach to Controlled 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 language model (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: https://arxiv.org/abs/1912.02164

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

Colab link to test it out without any setup: https://colab.research.google.com/drive/1Ux0Z4-ruiVtJ6jUk98uk6FqfvGHCOYL3

Setup

pip install -r requirements.txt

Citation

@article{dathathri2019plug, title={Plug and Play Language Models: a Simple Approach to Controlled Text Generation}, author={Sumanth Dathathri and Andrea Madotto and Janice Lan and Jane Hung and Eric Frank and Piero Molino and Jason Yosinski and Rosanne Liu}, journal={arXiv preprint arXiv:1912.02164}, year={2019}, } 

PPLM-BoW

Example command for bag-of-words control

python run_pplm.py -B military --cond_text "The potato" --length 50 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.03 --window_length 5 --kl_scale 0.01 --gm_scale 0.99 --colorama --sample

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 2 --cond_text "My dog died" --length 50 --gamma 1.0 --num_iterations 10 --num_samples 10 --stepsize 0.04 --kl_scale 0.01 --gm_scale 0.95 --sample

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

from Hacker News https://github.com/uber-research/PPLM