Persistent Anti-Muslim Bias in Large Language Models (2101.05783v2)

Abstract: It has been observed that large-scale LLMs capture undesirable societal biases, e.g. relating to race and gender; yet religious bias has been relatively unexplored. We demonstrate that GPT-3, a state-of-the-art contextual LLM, captures persistent Muslim-violence bias. We probe GPT-3 in various ways, including prompt completion, analogical reasoning, and story generation, to understand this anti-Muslim bias, demonstrating that it appears consistently and creatively in different uses of the model and that it is severe even compared to biases about other religious groups. For instance, "Muslim" is analogized to "terrorist" in 23% of test cases, while "Jewish" is mapped to "money" in 5% of test cases. We quantify the positive distraction needed to overcome this bias with adversarial text prompts, and find that use of the most positive 6 adjectives reduces violent completions for "Muslims" from 66% to 20%, but which is still higher than for other religious groups.

• The paper systematically quantifies anti-Muslim bias in GPT-3, revealing a 66% rate of violent completions and a 23% association with terrorism.
• It employs analogical reasoning and diverse prompt experiments to demonstrate how biased language outputs manifest in the model.
• Adversarial debiasing experiments show that adding positive adjectives can lower violent completions from 66% to 20%, indicating effective mitigation strategies.

Persistent Anti-Muslim Bias in LLMs

The paper "Persistent Anti-Muslim Bias in LLMs" by Abubakar Abid, Maheen Farooqi, and James Zou provides a critical analysis of biases embedded within GPT-3, a highly sophisticated transformer-based LLM. The authors specifically highlight the presence of anti-Muslim bias within GPT-3, asserting that such biases are pervasive and manifest creatively across various usage scenarios.

Key Findings

1. Quantification of Bias: The paper systematically quantifies the anti-Muslim bias ingrained in GPT-3. Using prompts like "Two Muslims walked into a" and running the model a hundred times, the authors observe violent language in 66% of the completions. In stark contrast, when substituting "Muslims" with other religious identifiers, they note a significant reduction in the generation of violent language. The data indicates that GPT-3 associates "Muslim" with "terrorist" 23% of the time without external embeddings, which is considerably higher than analogous stereotypes for other religious groups.
2. Analogical Reasoning: Abid et al. employ analogical reasoning tasks to assess language associations within the model. For example, "audacious is to boldness as Muslim is to..." yielded "terrorist" as a completion 23% of the time, showcasing a remarkable bias in GPT-3's associative capabilities towards a negative stereotype of Muslims. Comparative analysis with other religious groups shows no equivalent mapping frequency to a single, stereotypical noun.
3. Prompt Completion Bias: Utilizing OpenAI's GPT-3 Playground and API, the researchers demonstrate how the insertion of the word "Muslim" in various contexts often results in completions that are violent or biased. This indicates a deeper, underlying associative bias baked into the model's processing capabilities.
4. Adversarial Debiasing Experiments: Attempts to mitigate this bias through adversarial text prompts reveal some promise. By inserting positive adjectives before the prompt—e.g., "Muslims are hard-working"—the occurrence of violent completions was reduced from 66% to 46%, and further to 20% with optimal adjectives. However, these reductions still did not achieve parity with bias levels towards other religious identifiers.

Implications and Future Directions

The results convey crucial implications both for the immediate applications of LLMs and for broader NLP research trajectories:

• Practical Implications: The demonstration of such biases calls for developers to scrutinize LLM outputs closely, especially in applications that require unbiased language processing. Unintended perpetuation of stereotypes has the potential to harm real-world applications in media, customer service, education, and beyond.
• Theoretical Implications: This paper adds to the growing literature surrounding the inherent biases of AI systems, reaffirming the need for unbiased, diversified training datasets. It suggests that even with vast and varied data, models might still learn and propagate harmful stereotypes.
• Research Developments: Future research could focus on the training data and methodologies to mitigate such biases preemptively. Innovative techniques in debiasing LLMs through data diversification, adjusted pretraining strategies, or post-training fine-tuning need exploration.

This paper efficiently reveals the complex dynamics of bias in LLMs and serves as a call to action for the AI research community to intensify focus on debiasing methodologies. As AI continues to integrate further into society, understanding and resolving these biases is imperative to the ethical deployment of AI technologies.