Understanding and Addressing Bias in GPT-3 Language Models

The presence of bias in natural language processing (NLP) models has been a growing concern in recent years, and with the explosion in popularity of AI chatbots such as ChatGPT and other services including Microsoft’s Bing and Google’s Bard, we should be paying more attention to bias in AI models than ever before.

Abid, Farooqi, and Zou’s (2021) research sheds light on the presence of Muslim violence bias in GPT-3, one of the most advanced language models to date and the model on which ChatGPT is based.

GPT-3, which stands for “Generative Pre-trained Transformer 3”, is a state-of-the-art language processing model developed by OpenAI. It is an artificial intelligence model that is pre-trained on a large corpus of text data and can generate human-like responses to a wide range of natural language tasks, including language translation, chatbot conversations, and content creation. With its advanced machine learning algorithms, GPT-3 has attracted considerable attention from researchers, developers, and the media for its ability to perform complex language tasks. However, because it is a pre-trained model, it can also pick up biases present in those training datasets.

Abid et al. (2021) note that while race and gender bias have been studied to some extent, there has been little research on religious bias. To understand the nature of the bias in the GPT-3 model, they used several techniques, including prompt completion, analogical reasoning, and story generation. Using these methods, they show that the model consistently and persistently associates the word ‘Muslim’ with violence. For example, the word ‘Muslim’ is associated with the word ‘terrorist’ 23% of the time and the word ‘Jewish’ is associated with the word ‘money’ 5% of the time (see Figure 1). Of the 6 religious groups considered in this study, none is mapped to a single stereotypical noun with the same frequency as ‘Muslim’ is mapped to ‘terrorist’. They also show that prejudice against Muslims is stronger than prejudice against other religious groups.

To address this issue, Abid et al. (2021) suggest introducing positive associations with the word “Muslim” into the context. They found that using positive adjectives reduced violent completions for ‘Muslim’ from 66% to 20%. However, the bias against Muslims was still present to some extent, suggesting that there is still work to be done to fully mitigate such bias.

The study also highlights the power of language models to mutate biases in different ways, making them difficult to detect and mitigate. Therefore, the need to check language models for learned biases and unwanted linguistic associations is critical. The study highlights the importance of developing automated and optimised solutions to reduce bias in language models to ensure that they are inclusive, fair, and accurately reflect the diversity of the world’s population.

Overall, Abid, Farooqi and Zou’s (2021) study provides valuable insights into the challenges of developing inclusive and equitable language models. Their work highlights the potential impact of bias on societal attitudes and perceptions, and underscores the need to address these biases in language models. Their work is an important contribution to the growing body of research on bias in language models, and their proposed solution provides a starting point for future research and development in this area.

Reference

Abid, A., Farooqi, M., & Zou, J., (2021). Persistent Anti-Muslim Bias in Large Language Models. DOI: 10.48550/arxiv.2101.05783.