Toxicity in AI Text Generation

Toxicity in AI Text Generation

Toxicity in AI Text Generation. This article provides an overview of toxic language generation, what toxicity in text generation means, why it occurs, and how it is currently being addressed.

Why language models generate toxic outputs and what can be done about it

I recently implemented a small NLP project where I asked two open-domain chatbots the 36 questions to fall in loveWhat started as a pastime brought my attention to the issue of toxicity in AI text generation. I initially asked the language model (LM) GPT-2 the 36 questions. But I was shocked by some of the answers the model produced, which contained hateful and problematic language, and I decided not to publish its answers.

Aside from a couple of anecdotes about AI gone wrong, I hadn’t dealt with this side of text generation before. However, I realized that it is crucial to be aware of the potential harm caused by applying language models in user-facing projects. So I read up on why language models tend to create such hateful language and how this issue can be addressed.

Various research papers have investigated toxicity and social bias inherent in autoregressive LMs — such as GPT-2 [1] — and bidirectional encoder models — such as BERT [2]. This article provides an overview of toxic language generation and covers its main issues and solutions. *I discuss what toxicity in text generation means, why it occurs, and how it is currently being addressed. *I also address some ethical considerations related to detoxifying language models.

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