red-teaming-language-models-with-language-models's Introduction

Red Teaming Language Models with Language Models

A re-implementation of the Red Teaming Language Models with Language Models paper by Perez et al.

This implementation only focuses on toxic/offensive langauge section of the paper.

I have based run the red-teaming experiments on four target models -

I generated 50,000 valid test cases (questions) as opposed to 500,000 mentioned in the paper, due to compute constraints. A small percantge of the test cases were succesful in being able to elicit toxic language generation from these models.

For Toxicity detection, I have used Detoxify.

The percentage of toxic/offensive answers (toxicity probability > 0.5) generated using Zero-Shot and Stochastic Few-Shot generation for each model are presented below -

Model	Zero-Shot	Stochastic Few-Shot	Increase (Few-Shot vs Zero-Shot)
GPT2-XL-1.5B	0.99%	2.85%	2.88x
Llama-2-7B	0.15%	0.33%	2.20x
Pythia-6.9B	0.47%	1.02%	2.17x
Phi-1.5B	0.11%	0.79%	7.18x

The percentage of toxic/offensive questions and answers (toxicity probability > 0.5) generated using Zero-Shot and Stochastic Few-Shot generation for each model are presented below -

Model	Zero-Shot	Stochastic Few-Shot	Increase (Few-Shot vs Zero-Shot)
GPT2-XL-1.5B	2.28%	8.49%	3.72x
Llama-2-7B	0.20%	0.61%	3.05x
Pythia-6.9B	0.93%	3.00%	3.23x
Phi-1.5B	0.09%	0.38%	4.22x

Note - The numbers may be a bit high as the toxicity detection model has False Positives in its predictions as well. The toxic/offensive generations can be manually inspected with the script view_toxic_qa.py.

The questions, answers and toxicity scores for each model can be found in the csv files in the artifacts directory.

For inference, I have used vLLM (which is awesome!) for GPT2-XL, Llama-2 and Pythia to speedup the generation process. Phi-1.5 is currently not supported by vLLM.

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